research topics on energy sources

Renewable Energy

Renewable energy comes from sources that will not be used up in our lifetimes, such as the sun and wind.

Earth Science, Experiential Learning, Engineering, Geology

Wind Turbines in a Sheep Pasture

Wind turbines use the power of wind to generate energy. This is just one source of renewable energy.

Photograph by Jesus Keller/ Shutterstock

The wind, the sun, and Earth are sources of  renewable energy . These energy sources naturally renew, or replenish themselves.

Wind, sunlight, and the planet have energy that transforms in ways we can see and feel. We can see and feel evidence of the transfer of energy from the sun to Earth in the sunlight shining on the ground and the warmth we feel when sunlight shines on our skin. We can see and feel evidence of the transfer of energy in wind’s ability to pull kites higher into the sky and shake the leaves on trees. We can see and feel evidence of the transfer of energy in the geothermal energy of steam vents and geysers .

People have created different ways to capture the energy from these renewable sources.

Solar Energy

Solar energy can be captured “actively” or “passively.”

Active solar energy uses special technology to capture the sun’s rays. The two main types of equipment are photovoltaic cells (also called PV cells or solar cells) and mirrors that focus sunlight in a specific spot. These active solar technologies use sunlight to generate electricity , which we use to power lights, heating systems, computers, and televisions.

Passive solar energy does not use any equipment. Instead, it gets energy from the way sunlight naturally changes throughout the day. For example, people can build houses so their windows face the path of the sun. This means the house will get more heat from the sun. It will take less energy from other sources to heat the house.

Other examples of passive solar technology are green roofs , cool roofs, and radiant barriers . Green roofs are completely covered with plants. Plants can get rid of pollutants in rainwater and air. They help make the local environment cleaner.

Cool roofs are painted white to better reflect sunlight. Radiant barriers are made of a reflective covering, such as aluminum. They both reflect the sun’s heat instead of absorbing it. All these types of roofs help lower the amount of energy needed to cool the building.

Advantages and Disadvantages There are many advantages to using solar energy. PV cells last for a long time, about 20 years.

However, there are reasons why solar power cannot be used as the only power source in a community. It can be expensive to install PV cells or build a building using passive solar technology.

Sunshine can also be hard to predict. It can be blocked by clouds, and the sun doesn’t shine at night. Different parts of Earth receive different amounts of sunlight based on location, the time of year, and the time of day.

Wind Energy

People have been harnessing the wind’s energy for a long, long time. Five-thousand years ago, ancient Egyptians made boats powered by the wind. In 200 B.C.E., people used windmills to grind grain in the Middle East and pump water in China.

Today, we capture the wind’s energy with wind turbines . A turbine is similar to a windmill; it has a very tall tower with two or three propeller-like blades at the top. These blades are turned by the wind. The blades turn a generator (located inside the tower), which creates electricity.

Groups of wind turbines are known as wind farms . Wind farms can be found near farmland, in narrow mountain passes, and even in the ocean, where there are steadier and stronger winds. Wind turbines anchored in the ocean are called “ offshore wind farms.”

Wind farms create electricity for nearby homes, schools, and other buildings.

Advantages and Disadvantages Wind energy can be very efficient . In places like the Midwest in the United States and along coasts, steady winds can provide cheap, reliable electricity.

Another great advantage of wind power is that it is a “clean” form of energy. Wind turbines do not burn fuel or emit any pollutants into the air.

Wind is not always a steady source of energy, however. Wind speed changes constantly, depending on the time of day, weather , and geographic location. Currently, it cannot be used to provide electricity for all our power needs.

Wind turbines can also be dangerous for bats and birds. These animals cannot always judge how fast the blades are moving and crash into them.

Geothermal Energy

Deep beneath the surface is Earth’s core . The center of Earth is extremely hot—thought to be over 6,000 °C (about 10,800 °F). The heat is constantly moving toward the surface.

We can see some of Earth’s heat when it bubbles to the surface. Geothermal energy can melt underground rocks into magma and cause the magma to bubble to the surface as lava . Geothermal energy can also heat underground sources of water and force it to spew out from the surface. This stream of water is called a geyser.

However, most of Earth’s heat stays underground and makes its way out very, very slowly.

We can access underground geothermal heat in different ways. One way of using geothermal energy is with “geothermal heat pumps.” A pipe of water loops between a building and holes dug deep underground. The water is warmed by the geothermal energy underground and brings the warmth aboveground to the building. Geothermal heat pumps can be used to heat houses, sidewalks, and even parking lots.

Another way to use geothermal energy is with steam. In some areas of the world, there is underground steam that naturally rises to the surface. The steam can be piped straight to a power plant. However, in other parts of the world, the ground is dry. Water must be injected underground to create steam. When the steam comes to the surface, it is used to turn a generator and create electricity.

In Iceland, there are large reservoirs of underground water. Almost 90 percent of people in Iceland use geothermal as an energy source to heat their homes and businesses.

Advantages and Disadvantages An advantage of geothermal energy is that it is clean. It does not require any fuel or emit any harmful pollutants into the air.

Geothermal energy is only avaiable in certain parts of the world. Another disadvantage of using geothermal energy is that in areas of the world where there is only dry heat underground, large quantities of freshwater are used to make steam. There may not be a lot of freshwater. People need water for drinking, cooking, and bathing.

Biomass Energy

Biomass is any material that comes from plants or microorganisms that were recently living. Plants create energy from the sun through photosynthesis . This energy is stored in the plants even after they die.

Trees, branches, scraps of bark, and recycled paper are common sources of biomass energy. Manure, garbage, and crops , such as corn, soy, and sugar cane, can also be used as biomass feedstocks .

We get energy from biomass by burning it. Wood chips, manure, and garbage are dried out and compressed into squares called “briquettes.” These briquettes are so dry that they do not absorb water. They can be stored and burned to create heat or generate electricity.

Biomass can also be converted into biofuel . Biofuels are mixed with regular gasoline and can be used to power cars and trucks. Biofuels release less harmful pollutants than pure gasoline.

Advantages and Disadvantages A major advantage of biomass is that it can be stored and then used when it is needed.

Growing crops for biofuels, however, requires large amounts of land and pesticides . Land could be used for food instead of biofuels. Some pesticides could pollute the air and water.

Biomass energy can also be a nonrenewable energy source. Biomass energy relies on biomass feedstocks—plants that are processed and burned to create electricity. Biomass feedstocks can include crops, such as corn or soy, as well as wood. If people do not replant biomass feedstocks as fast as they use them, biomass energy becomes a non-renewable energy source.

Hydroelectric Energy

Hydroelectric energy is made by flowing water. Most hydroelectric power plants are located on large dams , which control the flow of a river.

Dams block the river and create an artificial lake, or reservoir. A controlled amount of water is forced through tunnels in the dam. As water flows through the tunnels, it turns huge turbines and generates electricity.

Advantages and Disadvantages Hydroelectric energy is fairly inexpensive to harness. Dams do not need to be complex, and the resources to build them are not difficult to obtain. Rivers flow all over the world, so the energy source is available to millions of people.

Hydroelectric energy is also fairly reliable. Engineers control the flow of water through the dam, so the flow does not depend on the weather (the way solar and wind energies do).

However, hydroelectric power plants are damaging to the environment. When a river is dammed, it creates a large lake behind the dam. This lake (sometimes called a reservoir) drowns the original river habitat deep underwater. Sometimes, people build dams that can drown entire towns underwater. The people who live in the town or village must move to a new area.

Hydroelectric power plants don’t work for a very long time: Some can only supply power for 20 or 30 years. Silt , or dirt from a riverbed, builds up behind the dam and slows the flow of water.

Other Renewable Energy Sources

Scientists and engineers are constantly working to harness other renewable energy sources. Three of the most promising are tidal energy , wave energy , and algal (or algae) fuel.

Tidal energy harnesses the power of ocean tides to generate electricity. Some tidal energy projects use the moving tides to turn the blades of a turbine. Other projects use small dams to continually fill reservoirs at high tide and slowly release the water (and turn turbines) at low tide.

Wave energy harnesses waves from the ocean, lakes, or rivers. Some wave energy projects use the same equipment that tidal energy projects do—dams and standing turbines. Other wave energy projects float directly on waves. The water’s constant movement over and through these floating pieces of equipment turns turbines and creates electricity.

Algal fuel is a type of biomass energy that uses the unique chemicals in seaweed to create a clean and renewable biofuel. Algal fuel does not need the acres of cropland that other biofuel feedstocks do.

Renewable Nations

These nations (or groups of nations) produce the most energy using renewable resources. Many of them are also the leading producers of nonrenewable energy: China, European Union, United States, Brazil, and Canada

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Top 10 areas of green energy research

Magesh Ganesan , Scientist, ACS International India Pvt. Ltd.

February 29, 2024

With so much being published on green energy and sustainability, how can you identify the fastest growing areas of research?  Learn how CAS Insights provides a unique view of the green energy research landscape and can help you identify emerging trends sooner.  Subscribe to be the first to know when we publish new scientific insights.

Fossil fuels remain our primary energy source, but their limited availability and negative environmental impact have led to events like the 2023 United Nations Climate Change Conference (COP28) to seek green energy alternatives. These are sources that can be replenished in the average human lifespan and have a net zero environmental impact.

Every year, millions of journal articles and patent applications are created, so it can be difficult to identify the signals from the noise. CAS curates, connects, and analyzes the world’s published science inside the CAS Content Collection™ to provide a unique view of the scientific landscape. This enables novel insights that show emerging trends in new research areas. Five broad areas of research were identified that contained the fastest-growing trends: batteries, hydrogen energy, solar cells, new materials, and photothermal energy.

How was the analysis done?

First, CAS identified almost one million indexed documents in the CAS Content Collection™ that were relevant to the green energy space. Then, our researchers analyzed the hidden connections between key concepts using advanced analytics, knowledge graphing, and natural language processing to identify emerging trends in this area. Finally, our expert scientists with dozens of years of experience derived unique insights from the landscape of connections created. Several exciting growth patterns emerged between 2018 and 2022 that are early indicators of opportunities ahead. While many areas of green energy are growing quickly, we identified and prioritized the top ten emerging topics (Figure 1) that will help us reach a more sustainable future.

Batteries, energy storage, and battery recycling

Batteries are the leading method of storing electricity worldwide. Lithium-ion batteries have become commonplace, being used in portable devices and electric vehicles due to their high energy density, while lead-acid batteries are conventionally used for portable and stationary power storage. However, lithium-ion batteries are a fire hazard while lead-acid batteries are notably toxic. This has led to researchers looking for alternate, safer ways to store electricity.

• Aqueous zinc-ion batteries: These batteries are being studied as alternatives to lead-acid batteries because they are naturally occurring, much more environmentally friendly, and notably cheaper and non-toxic.
• Solid-state lithium-ion battery: Standard lithium-ion batteries degrade quickly, are fire hazards, and have high toxicity. However, they are still widely used because of faster charging and easy manufacturing. Solid-state lithium-ion batteries can be charged and discharged many times more than lithium-ion batteries and hold more electricity.

The successful development of these new battery types will make the industry much safer. Not only will solid-state lithium batteries be less of a fire hazard, but the overall pollution will drop, owing to the absence of toxic liquid electrolytes and more sustainable production. Learn more about lithium-ion batteries , the landscape of recyclin g legislation and regulations , and the new breakthroughs  that are driving innovation. 

Hydrogen energy, green hydrogen economy, and hydrogen storage

Hydrogen has emerged as a promising alternative to fossil fuels, being more environmentally friendly, having higher energy per given weight than gasoline, and more applicable in many energy-related fields.

• Liquid hydrogen storage: Hydrogen has three times the gravimetric density of gasoline but only one-fourth of the volumetric energy density. This means liquid hydrogen is considered the most efficient method of storing hydrogen in its base form, and researchers are seeking new ways to take advantage of it. If properly harnessed, liquid hydrogen storage could enable new fuel cell-driven automobiles and decrease costs in petroleum refining, fertilizer production, and more. 
• Water splitting using heterojunction photocatalysts: Photocatalysts have emerged as a sustainable energy source, producing hydrogen using only water and sunlight. The main challenge, however, is identifying or developing them due to low efficiency and unsuitable band positions. Once these hurdles are overcome, the cost of hydrogen is expected to decrease, which could make it the preferred fuel source.   

The benefits of these hydrogen production and storage technologies are immense. Urea oxidation will be dual purpose, cleaning water and providing energy simultaneously. More efficient storage methods could facilitate the utilization of hydrogen fuel cells, revolutionizing commercial products like automobiles. These changes would be further bolstered by new water-splitting methods, which would make accessing the necessary hydrogen for these processes much cheaper. Learn more about photocatalysis and new breakthroughs in the landscape of green hydrogen production.  

Solar cells

Solar cells have seen an increased interest both academically and commercially. As industries look for more sustainable options, there will be more studies on how to optimize this technology for higher efficiency and lower cost.

• Non-fullerene acceptors for solar cells: The performance of organic solar cells has increased, but development is already underway to replace their most used acceptor, fullerene, with an alternative. These non-fullerene acceptors have more tunable properties, higher thermal and photochemical stability, and can lead to longer device lifetimes. This could lead to more stable, longer-lasting, and cheaper solar cells.
• Stable perovskite solar cells: As researchers look to enhance the efficiency of solar cells, one area of interest is perovskite-based solar cells. These easy-to-fabricate, low-cost cells have reported some of the highest energy efficiencies. However, the current materials used are unstable in certain conditions. The advantages of stable perovskite solar cells remain substantial and could diminish manufacturing costs if this challenge is overcome.

The main hurdle that these two innovations could overcome is cost. By cutting out fullerene or developing cells with perovskite, solar energy could be more affordable to many consumers. Learn more about emerging technologies in materials .

Sustainable chemistry, new materials, and greener alternatives

Noble and toxic metals are frequently used in the energy field. While functional, risks and challenges remain, hindering the industry’s progress. This has led studies to examine more sustainable and efficient alternatives.

• Mxenes: These are two-dimensional materials that incorporate a transition metal and a functional group. Their layered nature makes them strong candidates for energy storage applications like capacitors and batteries while their optical and catalytic properties have potential in photocatalysis and electrocatalysis. Mxenes also contain earth-abundant elements, circumventing the risks associated with noble or toxic metals. Any breakthrough with this material could result in significant cost, environmental, and energy storage benefits.
• Covalent organic frameworks: These are two or three-dimensional structures formed by organic precursor reactions. Forming covalently bonded, porous structures, they are being studied for hydrogen/methane and catalytic/electrocatalytic energy storage applications. Successful covalent organic framework applications could lead to many economic and environmental benefits in energy storage, chemical synthesis, catalysis, and gas separation with special interest in the automobile industry.

These materials have many possible applications. Both being made of earth-abundant materials, they will be more available and sustainable compared to other materials. By replacing current materials with covalent organic frameworks and mxenes, there will also be significant environmental and economic benefits across many industries. Learn more about sustainable catalysts , new biomaterials, and carbon nanotubes that can help build upon new opportunities ahead.

Solar energy, photothermal conversion, and green energy sources

As researchers try to find ways of reducing energy’s environmental impact, special interest is placed on renewable sources. Solar is already gaining commercial and industrial popularity, but further breakthroughs could lead to wider adoption.

• Photothermal energy conversion: The conversion of solar energy to heat, which generates steam, can generate electricity without using other sources. Researchers are studying inorganic and polymeric materials to find a suitable photothermal candidate. Success in this area could lead to a drastic energy cost reduction, owing to the sole reliance on solar energy. It would also have a substantial positive environmental impact by ideally removing the need for fossil fuels. 

Unlocking the potential of photothermal energy conversion could lead to immense energy cost savings and cleaner energy sources. Additionally, there has always been a desire to use solar energy to split water atoms for clean hydrogen production. This process of photocatalysis would be critical to using solar energy for clean hydrogen production and could reshape the future for a green hydrogen economy . 

Looking ahead

Green energy will remain a large research focus as we seek a net-zero environmental impact thanks to events like COP28. Suitable alternatives are being examined, but there remain challenges and risks that must be overcome before we see real-world applications. As more green energy advances are made, it can be challenging to keep up with the new developments. Subscribe to CAS Insights™ for unique views and the latest updates on green energy alternatives.

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By Hannah Ritchie, Pablo Rosado and Max Roser

The world lacks a safe, low-carbon, and cheap large-scale energy infrastructure.

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Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions

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• Perspective
• Published: 18 October 2022

Machine learning for a sustainable energy future

• Zhenpeng Yao   ORCID: orcid.org/0000-0001-8286-8257 1 , 2 , 3 , 4   na1 ,
• Yanwei Lum   ORCID: orcid.org/0000-0001-7261-2098 5 , 6   na1 ,
• Andrew Johnston 6   na1 ,
• Luis Martin Mejia-Mendoza 2 ,
• Xin Zhou 7 ,
• Yonggang Wen 7 ,
• Alán Aspuru-Guzik   ORCID: orcid.org/0000-0002-8277-4434 2 , 8 ,
• Edward H. Sargent   ORCID: orcid.org/0000-0003-0396-6495 6 &
• Zhi Wei Seh   ORCID: orcid.org/0000-0003-0953-567X 5  

Nature Reviews Materials volume  8 ,  pages 202–215 ( 2023 ) Cite this article

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• Computer science

Electrocatalysis

• Energy grids and networks
• Solar cells

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances — at the materials, devices and systems levels — for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

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Introduction

The combustion of fossil fuels, used to fulfill approximately 80% of the world’s energy needs, is the largest single source of rising greenhouse gas emissions and global temperature 1 . The increased use of renewable sources of energy, notably solar and wind power, is an economically viable path towards meeting the climate goals of the Paris Agreement 2 . However, the rate at which renewable energy has grown has been outpaced by ever-growing energy demand, and as a result the fraction of total energy produced by renewable sources has remained constant since 2000 (ref. 3 ). It is thus essential to accelerate the transition towards sustainable sources of energy 4 . Achieving this transition requires energy technologies, infrastructure and policies that enable and promote the harvest, storage, conversion and management of renewable energy.

In sustainable energy research, suitable material candidates (such as photovoltaic materials) must first be chosen from the combinatorial space of possible materials, then synthesized at a high enough yield and quality for use in devices (such as solar panels). The time frame of a representative materials discovery process is 15–20 years 5 , 6 , leaving considerable room for improvement. Furthermore, the devices have to be optimized for robustness and reproducibility to be incorporated into energy systems (such as in solar farms) 7 , where management of energy usage and generation patterns is needed to further guarantee commercial success.

Here we explore the extent to which machine learning (ML) techniques can help to address many of these challenges 8 , 9 , 10 . ML models can be used to predict specific properties of new materials without the need for costly characterization; they can generate new material structures with desired properties; they can understand patterns in renewable energy usage and generation; and they can help to inform energy policy by optimizing energy management at both device and grid levels.

In this Perspective, we introduce Acc(X)eleration Performance Indicators (XPIs), which can be used to measure the effectiveness of platforms developed for accelerated energy materials discovery. Next, we discuss closed-loop ML frameworks and evaluate the latest advances in applying ML to the development of energy harvesting, storage and conversion technologies, as well as the integration of ML into a smart power grid. Finally, we offer an overview of energy research areas that stand to benefit further from ML.

Performance indicators

Because many reports discuss ML-accelerated approaches for materials discovery and energy systems management, we posit that there should be a consistent baseline from which these reports can be compared. For energy systems management, performance indicators at the device, plant and grid levels have been reported 11 , 12 , yet there are no equivalent counterparts for accelerated materials discovery.

The primary goal in materials discovery is to develop efficient materials that are ready for commercialization. The commercialization of a new material requires intensive research efforts that can span up to two decades: the goal of every accelerated approach should be to accomplish commercialization an order-of-magnitude faster. The materials science field can benefit from studying the case of vaccine development. Historically, new vaccines take 10 years from conception to market 13 . However, after the start of the COVID-19 pandemic, several companies were able to develop and begin releasing vaccines in less than a year. This achievement was in part due to an unprecedented global research intensity, but also to a shift in the technology: after a technological breakthrough in 2008, the cost of sequencing DNA began decreasing exponentially 14 , 15 , enabling researchers to screen orders-of-magnitude more vaccines than was previously possible.

ML for energy technologies has much in common with ML for other fields like biomedicine, sharing the same methodology and principles. However, in practice, ML models for different technologies are exposed to additional unique requirements. For example, ML models for medical applications usually have complex structures that take into account regulatory oversight and ensure the safe development, use and monitoring of systems, which usually does not happen in the energy field 16 . Moreover, data availability varies substantially from field to field; biomedical researchers can work with a relatively large amount of data that energy researchers usually lack. This limited data accessibility can constrain the usage of sophisticated ML models (such as deep learning models) in the energy field. However, adaptation has been quick in all energy subfields, with a rapidly increased number of groups recognizing the importance of statistical methods and starting to use them for various problems. We posit that the use of high-throughput experimentation and ML in materials discovery workflows can result in breakthroughs in accelerating development, but the field first needs a set of metrics with which ML models can be evaluated and compared.

Accelerated materials discovery methods should be judged based on the time it takes for a new material to be commercialized. We recognize that this is not a useful metric for new platforms, nor is it one that can be used to decide quickly which platform is best suited for a particular scenario. We therefore propose here XPIs that new materials discovery platforms should report.

Acceleration factor of new materials, XPI-1

This XPI is evaluated by dividing the number of new materials that are synthesized and characterized per unit time with the accelerated platform by the number of materials that are synthesized and characterized with traditional methods. For example, an acceleration factor of ten means that for a given time period, the accelerated platform can evaluate ten times more materials than a traditional platform. For materials with multiple target properties, researchers should report the rate-limiting acceleration factor.

Number of new materials with threshold performance, XPI-2

This XPI tracks the number of new materials discovered with an accelerated platform that have a performance greater than the baseline value. The selection of this baseline value is critical: it should be something that fairly captures the standard to which new materials need to be compared. As an example, an accelerated platform that seeks to discover new perovskite solar cell materials should track the number of devices made with new materials that have a better performance than the best existing solar cell 17 .

Performance of best material over time, XPI-3

This XPI tracks the absolute performance — whether it is Faradaic efficiency, power conversion efficiency or other — of the best material as a function of time. For the accelerated framework, the evolution of the performance should increase faster than the performance obtained by traditional methods 18 .

Repeatability and reproducibility of new materials, XPI-4

This XPI seeks to ensure that the new materials discovered are consistent and repeatable: this is a key consideration to screen out materials that would fail at the commercialization stage. The performance of a new material should not vary by more than x % of its mean value (where x is the standard error): if it does, this material should not be included in either XPI-2 (number of new materials with threshold performance) or XPI-3 (performance of best material over time).

Human cost of the accelerated platform, XPI-5

This XPI reports the total costs of the accelerated platform. This should include the total number of researcher hours needed to design and order the components for the accelerated system, develop the programming and robotic infrastructure, develop and maintain databases used in the system and maintain and run the accelerated platform. This metric would provide researchers with a realistic estimate of the resources required to adapt an accelerated platform for their own research.

Use of the XPIs

Each of these XPIs can be measured for computational, experimental or integrated accelerated systems. Consistently reporting each of these XPIs as new accelerated platforms are developed will allow researchers to evaluate the growth of these platforms and will provide a consistent metric by which different platforms can be compared. As a demonstration, we applied the XPIs to evaluate the acceleration performance of several typical platforms: Edisonian-like trial-test, robotic photocatalysis development 19 and design of a DNA-encoded-library-based kinase inhibitor 20 (Table  1 ). To obtain a comprehensive performance estimate, we define one overall acceleration score S adhering to the following rules. The dependent acceleration factors (XPI-1 and XPI-2), which function in a synergetic way, are added together to reflect their contribution as a whole. The independent acceleration factors (XPI-3, XPI-4 and XPI-5), which may function in a reduplicated way, are multiplied together to value their contributions respectively. As a result, the overall acceleration score can be calculated as S  = (XPI-1 + XPI-2) × XPI-3 × XPI-4 ÷ XPI-5. As the reference, the Edisonian-like approach has a calculated overall XPIs score of around 1, whereas the most advanced method, the DNA-encoded-library-based drug design, exhibits an overall XPIs score of 10 7 . For the sustainability field, the robotic photocatalysis platform has an overall XPIs score of 10 5 .

For energy systems, the most frequently reported XPI is the acceleration factor, in part because it is deterministic, but also because it is easy to calculate at the end of the development of a workflow. In most cases, we expect that authors report the acceleration factor only after completing the development of the platform. Reporting the other suggested XPIs will provide researchers with a better sense of both the time and human resources required to develop the platform until it is ready for publication. Moving forward, we hope that other researchers adopt the XPIs — or other similar metrics — to allow for fair and consistent comparison between the different methods and algorithms that are used to accelerate materials discovery.

Closed-loop ML for materials discovery

The traditional approach to materials discovery is often Edisonian-like, relying on trial and error to develop materials with specific properties. First, a target application is identified, and a starting pool of possible candidates is selected (Fig.  1a ). The materials are then synthesized and incorporated into a device or system to measure their properties. These results are then used to establish empirical structure–property relationships, which guide the next round of synthesis and testing. This slow process goes through as many iterations as required and each cycle can take several years to complete.

a | Traditional Edisonian-like approach, which involves experimental trial and error. b | High-throughput screening approach involving a combination of theory and experiment. c | Machine learning (ML)-driven approach whereby theoretical and experimental results are used to train a ML model for predicting structure–property relationships. d | ML-driven approach for property-directed and automatic exploration of the chemical space using optimization ML (such as genetic algorithms or generative models) that solve the ‘inverse’ design problem.

A computation-driven, high-throughput screening strategy (Fig.  1b ) offers a faster turnaround. To explore the overall vast chemical space (~10 60 possibilities), human intuition and expertise can be used to create a library with a substantial number of materials of interest (~10 4 ). Theoretical calculations are carried out on these candidates and the top performers (~10 2 candidates) are then experimentally verified. With luck, the material with the desired functionality is ‘discovered’. Otherwise, this process is repeated in another region of the chemical space. This approach can still be very time-consuming and computationally expensive and can only sample a small region of the chemical space.

ML can substantially increase the chemical space sampled, without costing extra time and effort. ML is data-driven, screening datasets to detect patterns, which are the physical laws that govern the system. In this case, these laws correspond to materials structure–property relationships. This workflow involves high-throughput virtual screening (Fig.  1c ) and begins by selecting a larger region (~10 6 ) of the chemical space of possibilities using human intuition and expertise. Theoretical calculations are carried out on a representative subset (~10 4 candidates) and the results are used for training a discriminative ML model. The model can then be used to make predictions on the other candidates in the overall selected chemical space 9 . The top ~10 2 candidates are experimentally verified, and the results are used to improve the predictive capabilities of the model in an iterative loop. If the desired material is not ‘discovered’, the process is repeated on another region of the chemical space.

An improvement on the previous approaches is a framework that requires limited human intuition or expertise to direct the chemical space search: the automated virtual screening approach (Fig.  1d ). To begin with, a region of the chemical space is picked at random to initiate the process. Thereafter, this process is similar to the previous approach, except that the computational and experimental data is also used to train a generative learning model. This generative model solves the ‘inverse’ problem: given a required property, the goal is to predict an ideal structure and composition in the chemical space. This enables a directed, automated search of the chemical space, towards the goal of ‘discovering’ the ideal material 8 .

ML for energy

ML has so far been used to accelerate the development of materials and devices for energy harvesting (photovoltaics), storage (batteries) and conversion (electrocatalysis), as well as to optimize power grids. Besides all the examples discussed here, we summarize the essential concepts in ML (Box  1 ), the grand challenges in sustainable materials research (Box  2 ) and the details of key studies (Table  2 ).

Box 1 Essential concepts in ML

With the availability of large datasets 122 , 125 and increased computing power, various machine learning (ML) algorithms have been developed to solve diverse problems in energy. Below, we provide a brief overview of the types of problem that ML can solve in energy technology, and we then summarize the status of ML-driven energy research. More detailed information about the nuts and bolts of ML techniques can be found in previous reviews 173 , 174 , 175 .

Property prediction

Supervised learning models are predictive (or discriminative) models that are given a datapoint x , and seek to predict a property y (for example, the bandgap 27 ) after being trained on a labelled dataset. The property y can be either continuous or discrete. These models have been used to aid or even replace physical simulations or measurements under certain circumstances 176 , 177 .

Generative materials design

Unsupervised learning models are generative models that can generate or output new examples x ′ (such as new molecules 104 ) after being trained on an unlabelled dataset. This generation of new examples can be further enhanced with additional information (physical properties) to condition or bias the generative process, allowing the models to generate examples with improved properties and leading to the property-to-structure approach called inverse design 52 , 178 .

Self-driving laboratories

Self-driving or autonomous laboratories 19 use ML models to plan and perform experiments, including the automation of retrosynthesis analysis (such as in reinforcement-learning-aided synthesis planning 124 , 179 ), prediction of reaction products (such as in convolutional neural networks (CNNs) for reaction prediction 137 , 138 ) and reaction condition optimization (such as in robotic workflows optimized by active learning 19 , 160 , 180 , 181 , 182 , 183 ). Self-driving laboratories, which use active learning for iterating through rounds of synthesis and measurements, are a key component in the closed-loop inverse design 52 .

Aiding characterization

ML models have been used to aid the quantitative or qualitative analysis of experimental observations and measurements, including assisting in the determination of crystal structure from transmission electron microscopy images 184 , identifying coordination environment 81 and structural transition 83 from X-ray absorption spectroscopy and inferring crystal symmetry from electron diffraction 176 .

Accelerating theoretical computations

ML models can enable otherwise intractable simulations by reducing the computational cost (processor core amount and time) for systems with increased length and timescales 69 , 70 and providing potentials and functionals for complex interactions 68 .

Optimizing system management

ML models can aid the management of energy systems at the device or grid power level by predicting lifetimes (such as battery life 43 , 44 ), adapting to new loads (such as in long short-term memory for building load prediction 95 ) and optimizing performance (such as in reinforcement learning for smart grid control 94 ).

Box 2 Grand challenges in energy materials research

Photovoltaics.

Discover non-toxic (Pd- and Cd-free) materials with good optoelectronic properties

Identify and minimize materials defects in light-absorbing materials

Design effective recombination-layer materials for tandem solar cells

Develop materials design strategies for long-term operational stability 125

Develop (hole/electron) transport materials with high carrier mobility 125

Optimize cell structure for maximum light absorption and minimum use of active materials

Tune materials bandgaps for optimal solar-harvesting performance under complex operation conditions 21 , 22

Develop Earth-abundant cathode materials (Co-free) with high reversibility and charge capacity 4

Design electrolytes with wider electrochemical windows and high conductivity 4

Identify electrolyte systems to boost battery performance and lifetime 4

Discover new molecules for redox flow batteries with suitable voltage 4

Understand correlation between defect growth in battery materials and overall degradation process of battery components

Tune operando (dis)charging protocol for minimized capacity loss, (dis)charging rate and optimal battery life under diversified conditions 7 , 53

Design materials with optimal adsorption energy for maximized catalytic activity 60 , 61

Identify and study active sites on catalytic materials 58

Engineer catalytic materials for extended durability 58 , 60 , 61

Identify a fuller set of materials descriptors that relate to catalytic activity 60 , 61

Engineer multiple catalytic functionalities into the same material 60 , 61

Design multiscale electrode structures for optimized catalytic activity

Correlate atomistic contamination and growth of catalyst particles with electrode degradation process

Tune operando (dis)charging protocol for minimized capacity loss and optimal cell life

ML is accelerating the discovery of new optoelectronic materials and devices for photovoltaics, but major challenges are still associated with each step.

Photovoltaics materials discovery

One materials class for which ML has proved particularly effective is perovskites, because these materials have a vast chemical space from which the constituents may be chosen. Early representations of perovskite materials for ML were atomic-feature representations, in which each structure is encoded as a fixed-length vector comprised of an average of certain atomic properties of the atoms in the crystal structure 21 , 22 . A similar technique was used to predict new lead-free perovskite materials with the proper bandgap for solar cells 23 (Fig.  2a ). These representations allowed for high accuracy but did not account for any spatial relation between atoms 24 , 25 . Materials systems can also be represented as images 26 or as graphs 27 , enabling the treatment of systems with diverse number of atoms. The latter representation is particularly compelling, as perovskites, particularly organic–inorganic perovskites, have crystal structures that incorporate a varying number of atoms, and the organic molecules can vary in size.

a | Energy harvesting 23 . b | Energy storage 38 . c | Energy conversion 76 . d | Energy management 93 . ICSD, Inorganic Crystal Structure Database; ML, machine learning.

Although bandgap prediction is an important first step, this parameter alone is not sufficient to indicate a useful optoelectronic material; other parameters, including electronic defect density and stability, are equally important. Defect energies are addressable with computational methods, but the calculation of defects in structures is extremely computationally expensive, which inhibits the generation of a dataset of defect energies from which an ML model can be trained. To expedite the high-throughput calculation of defect energies, a Python toolkit has been developed 28 that will be pivotal in building a database of defect energies in semiconductors. Researchers can then use ML to predict both the formation energy of defects and the energy levels of these defects. This knowledge will ensure that the materials selected from high-throughput screening will not only have the correct bandgap but will also either be defect-tolerant or defect-resistant, finding use in commercial optoelectronic devices.

Even without access to a large dataset of experimental results, ML can accelerate the discovery of optoelectronic materials. Using a self-driving laboratory approach, the number of experiments required to optimize an organic solar cell can be reduced from 500 to just 60 (ref. 29 ). This robotic synthesis method accelerates the learning rate of the ML models and drastically reduces the cost of the chemicals needed to run the optimization.

Solar device structure and fabrication

Photovoltaic devices require optimization of layers other than the active layer to maximize performance. One component is the top transparent conductive layer, which needs to have both high optical transparency and high electronic conductivity 30 , 31 . A genetic algorithm that optimized the topology of a light-trapping structure enabled a broadband absorption efficiency of 48.1%, which represents a more than threefold increase over the Yablonovitch limit, the 4 n 2 factor (where n is the refractive index of the material) theoretical limit for light trapping in photovoltaics 32 .

A universal standard irradiance spectrum is usually used by researchers to determine optimal bandgaps for solar cell operation 33 . However, actual solar irradiance fluctuates based on factors such as the position of the Sun, atmospheric phenomena and the season. ML can reduce yearly spectral sets into a few characteristic spectra 33 , allowing for the calculation of optimal bandgaps for real-world conditions.

To optimize device fabrication, a CNN was used to predict the current–voltage characteristics of as-cut Si wafers based on their photoluminescence images 34 . Additionally, an artificial neural network was used to predict the contact resistance of metallic front contacts for Si solar cells, which is critical for the manufacturing process 35 .

Although successful, these studies appear to be limited to optimizing structures and processes that are already well established. We suggest that, in future work, ML could be used to augment simulations, such as the multiphysics models for solar cells. Design of device architecture could begin from such simulation models, coupled with ML in an iterative process to quickly optimize design and reduce computational time and cost. In addition, optimal conditions for the scaling-up of device area and fabrication processes are likely to be very different from those for laboratory-scale demonstrations. However, determining these optimal conditions could be expensive in terms of materials cost and time, owing to the need to construct much larger devices. In this regard, ML, together with the strategic design of experiments, could greatly accelerate the optimization of process conditions (such as the annealing temperatures and solvent choice).

Electrochemical energy storage

Electrochemical energy storage is an essential component in applications such as electric vehicles, consumer electronics and stationary power stations. State-of-the-art electrochemical energy storage solutions have varying efficacy in different applications: for example, lithium-ion batteries exhibit excellent energy density and are widely used in electronics and electric vehicles, whereas redox flow batteries have drawn substantial attention for use in stationary power storage. ML approaches have been widely employed in the field of batteries, including for the discovery of new materials such as solid-state ion conductors 36 , 37 , 38 (Fig.  2b ) and redox active electrolytes for redox flow batteries 39 . ML has also aided battery management, for example, through state-of-charge determination 40 , state-of-health evaluation 41 , 42 and remaining-life prediction 43 , 44 .

Electrode and electrolyte materials design

Layered oxide materials, such as LiCoO 2 or LiNi x Mn y Co 1- x - y O 2 , have been used extensively as cathode materials for alkali metal-ion (Li/Na/K) batteries. However, developing new Li-ion battery materials with higher operating voltages, enhanced energy densities and longer lifetimes is of paramount interest. So far, universal design principles for new battery materials remain undefined, and hence different approaches have been explored. Data from the Materials Project have been used to model the electrode voltage profile diagrams for different materials in alkali metal-ion batteries (Na and K) 45 , leading to the proposition of 5,000 different electrode materials with appropriate moderate voltages. ML was also employed to screen 12,000 candidates for solid Li-ion batteries, resulting in the discovery of ten new Li-ion conducting materials 46 , 47 .

Flow batteries consist of active materials dissolved in electrolytes that flow into a cell with electrodes that facilitate redox reactions. Organic flow batteries are of particular interest. In flow batteries, the solubility of the active material in the electrolyte and the charge/discharge stability dictate performance. ML methods have explored the chemical space to find suitable electrolytes for organic redox flow batteries 48 , 49 . Furthermore, a multi-kernel-ridge regression method accelerated the discovery of active organic molecules using multiple feature training 48 . This method also helped in predicting the solubility dependence of anthraquinone molecules with different numbers and combinations of sulfonic and hydroxyl groups on pH. Future opportunities lie in the exploration of large combinatorial spaces for the inverse design of high-entropy electrodes 50 and high-voltage electrolytes 51 . To this end, deep generative models can assist the discovery of new materials based on the simplified molecular input line entry system (SMILES) representation of molecules 52 .

Battery device and stack management

A combination of mechanistic and semi-empirical models is currently used to estimate capacity and power loss in lithium-ion batteries. However, the models are applicable only to specific failure mechanisms or situations and cannot predict the lifetimes of batteries at the early stages of usage. By contrast, mechanism-agnostic models based on ML can accurately predict battery cycle life, even at an early stage of a battery’s life 43 . A combined early-prediction and Bayesian optimization model has been used to rapidly identify the optimal charging protocol with the longest cycle life 44 . ML can be used to accelerate the optimization of lithium-ion batteries for longer lifetimes 53 , but it remains to be seen whether these models can be generalized to different battery chemistries 54 .

ML methods can also predict important properties of battery storage facilities. A neural network was used to predict the charge/discharge profiles in two types of stationary battery systems, lithium iron phosphate and vanadium redox flow batteries 55 . Battery power management techniques must also consider the uncertainty and variability that arise from both the environment and the application. An iterative Q -learning ( reinforcement learning ) method was also designed for battery management and control in smart residential environments 56 . Given the residential load and the real-time electricity rate, the method is effective at optimizing battery charging/discharging/idle cycles. Discriminative neural network-based models can also optimize battery usage in electric vehicles 57 .

Although ML is able to predict the lifetime of batteries, the underlying degradation mechanisms are difficult to identify and correlate to the state of health and lifetime. To this end, incorporation of domain knowledge into a hybrid physics-based ML model can provide insight and reduce overfitting 53 . However, incorporating the physics of battery degradation processes into a hybrid model remains challenging; representation of electrode materials that encode both compositional and structural information is far from trivial. Validation of these models also requires the development of operando characterization techniques, such as liquid-phase transmission electron microscopy and ambient-pressure X-ray absorption spectroscopy (XAS), that reflect true operating conditions as closely as possible 54 . Ideally, these characterization techniques should be carried out in a high-throughput manner, using automated sample changers, for example, in order to generate large datasets for ML.

Electrocatalysts

Electrocatalysis enables the conversion of simple feedstocks (such as water, carbon dioxide and nitrogen) into valuable chemicals and/or fuels (such as hydrogen, hydrocarbons and ammonia), using renewable energy as an input 58 . The reverse reactions are also possible in a fuel cell, and hydrogen can be consumed to produce electricity 59 . Active and selective electrocatalysts must be developed to improve the efficiency of these reactions 60 , 61 . ML has been used to accelerate electrocatalyst development and device optimization.

Electrocatalyst materials discovery

The most common descriptor of catalytic activity is the adsorption energy of intermediates on a catalyst 61 , 62 . Although these adsorption energies can be calculated using density functional theory (DFT), catalysts possess multiple surface binding sites, each with different adsorption energies 63 . The number of possible sites increases dramatically if alloys are considered, and thus becomes intractable with conventional means 64 .

DFT calculations are critical for the search of electrocatalytic materials 65 and efforts have been made to accelerate the calculations and to reduce their computational cost by using surrogate ML models 66 , 67 , 68 , 69 . Complex reaction mechanisms involving hundreds of possible species and intermediates can also be simplified using ML, with a surrogate model predicting the most important reaction steps and deducing the most likely reaction pathways 70 . ML can also be used to screen for active sites across a random, disordered nanoparticle surface 71 , 72 . DFT calculations are performed on only a few representative sites, which are then used to train a neural network to predict the adsorption energies of all active sites.

Catalyst development can benefit from high-throughput systems for catalyst synthesis and performance evaluation 73 , 74 . An automatic ML-driven framework was developed to screen a large intermetallic chemical space for CO 2 reduction and H 2 evolution 75 . The model predicted the adsorption energy of new intermetallic systems and DFT was automatically performed on the most promising candidates to verify the predictions. This process went on iteratively in a closed feedback loop. 131 intermetallic surfaces across 54 alloys were ultimately identified as promising candidates for CO 2 reduction. Experimental validation 76 with Cu–Al catalysts yielded an unprecedented Faradaic efficiency of 80% towards ethylene at a high current density of 400 mA cm – 2 (Fig.  2c ).

Because of the large number of properties that electrocatalysts may possess (such as shape, size and composition), it is difficult to do data mining on the literature 77 . Electrocatalyst structures are complex and difficult to characterize completely; as a result, many properties may not be fully characterized by research groups in their publications. To avoid situations in which potentially promising compositions perform poorly as a result of non-ideal synthesis or testing conditions, other factors (such as current density, particle size and pH value) that affect the electrocatalyst performance must be kept consistent. New approaches such as carbothermal shock synthesis 78 , 79 may be a promising avenue, owing to its propensity to generate uniformly sized and shaped alloy nanoparticles, regardless of composition.

XAS is a powerful technique, especially for in situ measurements, and has been widely employed to gain crucial insight into the nature of active sites and changes in the electrocatalyst over time 80 . Because the data analysis relies heavily on human experience and expertise, there has been interest in developing ML tools for interpreting XAS data 81 . Improved random forest models can predict the Bader charge (a good approximation of the total electronic charge of an atom) and nearest-neighbour distances, crucial factors that influence the catalytic properties of the material 82 . The extended X-ray absorption fine structure (EXAFS) region of XAS spectra is known to contain information on bonding environments and coordination numbers. Neural networks can be used to automatically interpret EXAFS data 83 , permitting the identification of the structure of bimetallic nanoparticles using experimental XAS data, for example 84 . Raman and infrared spectroscopy are also important tools for the mechanistic understanding of electrocatalysis. Together with explainable artificial intelligence (AI), which can relate the results to underlying physics, these analyses could be used to discover descriptors hidden in spectra that could lead to new breakthroughs in electrocatalyst discovery and optimization.

Fuel cell and electrolyser device management

A fuel cell is an electrochemical device that can be used to convert the chemical energy of a fuel (such as hydrogen) into electrical energy. An electrolyser transforms electrical energy into chemical energy (such as in water splitting to generate hydrogen). ML has been used to optimize and manage their performance, predict degradation and device lifetime as well as detect and diagnose faults. Using a hybrid method consisting of an extreme learning machine, genetic algorithms and wavelet analysis, the degradation in proton-exchange membrane fuel cells has been predicted 85 , 86 . Electrochemical impedance measurements used as input for an artificial neural network have enabled fault detection and isolation in a high-temperature stack of proton-exchange membrane fuel cells 87 , 88 .

ML approaches can also be employed to diagnose faults, such as fuel and air leakage issues, in solid oxide fuel cell stacks. Artificial neural networks can predict the performance of solid oxide fuel cells under different operating conditions 89 . In addition, ML has been applied to optimize the performance of solid oxide electrolysers, for CO 2 /H 2 O reduction 90 , and chloralkali electrolysers 91 .

In the future, the use of ML for fuel cells could be combined with multiscale modelling to improve their design, for example to minimize Ohmic losses and optimize catalyst loading. For practical applications, fuel cells may be subject to fluctuations in energy output requirements (for example, when used in vehicles). ML models could be used to determine the effects of such fluctuations on the long-term durability and performance of fuel cells, similar to what has been done for predicting the state of health and lifetime for batteries. Furthermore, it remains to be seen whether the ML techniques for fuel cells can be easily generalized to electrolysers and vice versa, using transfer learning for example, given that they are essentially reactions in reverse.

Smart power grids

A power grid is responsible for delivering electrical energy from producers (such as power plants and solar farms) to consumers (such as homes and offices). However, energy fluctuations from intermittent renewable energy generators can render the grid vulnerable 92 . ML algorithms can be used to optimize the automatic generation control of power grids, which controls the power output of multiple generators in an energy system. For example, when a relaxed deep learning model was used as a unified timescale controller for the automatic generation control unit, the total operational cost was reduced by up to 80% compared with traditional heuristic control strategies 93 (Fig.  2d ). A smart generation control strategy based on multi-agent reinforcement learning was found to improve the control performance by around 10% compared with other ML algorithms 94 .

Accurate demand and load prediction can support decision-making operations in energy systems for proper load scheduling and power allocation. Multiple ML methods have been proposed to precisely predict the demand load: for example, long short-term memory was used to successfully and accurately predict hourly building load 95 . Short-term load forecasting of diverse customers (such as retail businesses) using a deep neural network and cross-building energy demand forecasting using a deep belief network have also been demonstrated effectively 96 , 97 .

Demand-side management consists of a set of mechanisms that shape consumer electricity consumption by dynamically adjusting the price of electricity. These include reducing (peak shaving), increasing (load growth) and rescheduling (load shifting) the energy demand, which allows for flexible balancing of renewable electricity generation and load 98 . A reinforcement-learning-based algorithm resulted in substantial cost reduction for both the service provider and customer 99 . A decentralized learning-based residential demand scheduling technique successfully shifted up to 35% of the energy demand to periods of high wind availability, substantially saving power costs compared with the unscheduled energy demand scenario 100 . Load forecasting using a multi-agent approach integrates load prediction with reinforcement learning algorithms to shift energy usage (for example, to different electrical devices in a household) for its optimization 101 . This approach reduced peak usage by more than 30% and increased off-peak usage by 50%, reducing the cost and energy losses associated with energy storage.

Opportunities for ML in renewable energy

ML provides the opportunity to enable substantial further advances in different areas of the energy materials field, which share similar materials-related challenges (Fig.  3 ). There are also grand challenges for ML application in smart grid and policy optimization.

a | Energy materials present additional modelling challenges. Machine learning (ML) could help in the representation of structurally complex structures, which can include disordering, dislocations and amorphous phases. b | Flexible models that scale efficiently with varied dataset sizes are in demand, and ML could help to develop robust predictive models. The yellow dots stand for the addition of unreliable datasets that could harm the prediction accuracy of the ML model. c | Synthesis route prediction remains to be solved for the design of a novel material. In the ternary phase diagram, the dots stand for the stable compounds in that corresponding phase space and the red dot for the targeted compound. Two possible synthesis pathways are compared for a single compound. The score obtained would reflect the complexity, cost and so on of one synthesis pathway. d | ML-aided phase degradation prediction could boost the development of materials with enhanced cyclability. The shaded region represents the rocksalt phase, which grows inside the layered phase. The arrow marks the growth direction. e | The use of ML models could help in optimizing energy generation and energy consumption. Automating the decision-making processes associated with dynamic power supplies using ML will make the power distribution more efficient. f | Energy policy is the manner in which an entity (for example, a government) addresses its energy issues, including conversion, distribution and utilization, where ML could be used to optimize the corresponding economy.

Materials with novel geometries

A ML representation is effective when it captures the inherent properties of the system (such as its physical symmetries) and can be utilized in downstream ancillary tasks, such as transfer learning to new predictive tasks, building new knowledge using visualization or attribution and generating similar data distributions with generative models 102 .

For materials, the inputs are molecules or crystal structures whose physical properties are modelled by the Schrödinger equation. Designing a general representation of materials that reflects these properties is an ongoing research problem. For molecular systems, several representations have been used successfully, including fingerprints 103 , SMILES 104 , self-referencing embedded strings (SELFIES) 105 and graphs 106 , 107 , 108 . Representing crystalline materials has the added complexity of needing to incorporate periodicity in the representation. Methods like the smooth overlap of atomic positions 109 , Voronoi tessellation 110 , 111 , diffraction images 112 , multi-perspective fingerprints 113 and graph-based algorithms 27 , 114 have been suggested, but typically lack the capability for structure reconstruction.

Complex structural systems found in energy materials present additional modelling challenges (Fig.  3a ): a large number of atoms (such as in reticular frameworks or polymers), specific symmetries (such as in molecules with a particular space group and for reticular frameworks belonging to a certain topology), atomic disordering, partial occupancy, or amorphous phases (leading to an enormous combinatorial space), defects and dislocations (such as interfaces and grain boundaries) and low-dimensionality materials (as in nanoparticles). Reduction approximations alleviate the first issue (using, for example, RFcode for reticular framework representation) 8 , but the remaining several problems warrant intensive future research efforts.

Self- supervised learning , which seeks to lever large amounts of synthetic labels and tasks to continue learning without experimental labels 115 , multi-task learning 116 , in which multiple material properties can be modelled jointly to exploit correlation structure between properties, and meta-learning 117 , which looks at strategies that allow models to perform better in new datasets or in out-of-distribution data, all offer avenues to build better representations. On the modelling front, new advances in attention mechanisms 118 , 119 , graph neural networks 120 and equivariant neural networks 121 expand our range of tools with which to model interactions and expected symmetries.

Robust predictive models

Predictive models are the first step when building a pipeline that seeks materials with desired properties. A key component for building these models is training data; more data will often translate into better-performing models, which in turn will translate into better accuracy in the prediction of new materials. Deep learning models tend to scale more favourably with dataset size than traditional ML approaches (such as random forests). Dataset quality is also essential. However, experiments are usually conducted under diverse conditions with large variation in untracked variables (Fig.  3b ). Additionally, public datasets are more likely to suffer from publication bias, because negative results are less likely to be published even though they are just as important as positive results when training statistical models 122 .

Addressing these issues require transparency and standardization of the experimental data reported in the literature. Text and natural language processing strategies could then be employed to extract data from the literature 77 . Data should be reported with the belief that it will eventually be consolidated in a database, such as the MatD3 database 123 . Autonomous laboratory techniques will help to address this issue 19 , 124 . Structured property databases such as the Materials Project 122 and the Harvard Clean Energy Project 125 can also provide a large amount of data. Additionally, different energy fields — energy storage, harvesting and conversion — should converge upon a standard and uniform way to report data. This standard should be continuously updated; as researchers continue to learn about the systems they are studying, conditions that were previously thought to be unimportant will become relevant.

New modelling approaches that work in low-data regimes, such as data-efficient models, dataset-building strategies (active sampling) 126 and data-augmentation techniques, are also important 127 . Uncertainty quantification , data efficiency, interpretability and regularization are important considerations that improve the robustness of ML models. These considerations relate to the notion of generalizability: predictions should generalize to a new class of materials that is out of the distribution of the original dataset. Researchers can attempt to model how far away new data points are from the training set 128 or the variability in predicted labels with uncertainty quantification 129 . Neural networks are a flexible model class, and often models can be underspecified 130 . Incorporating regularization, inductive biases or priors can boost the credibility of a model. Another way to create trustable models could be to enhance the interpretability of ML algorithms by deriving feature relevance and scoring their importance 131 . This strategy could help to identify potential chemically meaningful features and form a starting point for understanding latent factors that dominate material properties. These techniques can also identify the presence of model bias and overfitting, as well as improving generalization and performance 132 , 133 , 134 .

Stable and synthesizable new materials

The formation energy of a compound is used to estimate its stability and synthesizability 135 , 136 . Although negative values usually correspond to stable or synthesizable compounds, slightly positive formation energies below a limit lead to metastable phases with unclear synthesizability 137 , 138 . This is more apparent when investigating unexplored chemical spaces with undetermined equilibrium ground states; yet often the metastable phases exhibit superior properties, as seen in photovoltaics 136 , 139 and ion conductors 140 , for example. It is thus of interest to develop a method to evaluate the synthesizability of metastable phases (Fig.  3c ). Instead of estimating the probability that a particular phase can be synthesized, one can instead evaluate its synthetic complexity using ML. In organic chemistry, synthesis complexity is evaluated according to the accessibility of the phases’ synthesis route 141 or precedent reaction knowledge 142 . Similar methodologies can be applied to the inorganic field with the ongoing design of automated synthesis-planning algorithms for inorganic materials 143 , 144 .

Synthesis and evaluation of a new material alone does not ensure that material will make it to market; material stability is a crucial property that takes a long time to evaluate. Degradation is a generally complex process that occurs through the loss of active matter or growth of inactive phases (such as the rocksalt phases formed in layered Li-ion battery electrodes 145 (Fig.  3d ) or the Pt particle agglomeration in fuel cells 146 ) and/or propagation of defects (such as cracks in cycled battery electrode 147 ). Microscopies such as electron microscopy 148 and simulations such as continuum mechanics modelling 149 are often used to investigate growth and propagation dynamics (that is, phase boundary and defect surface movements versus time). However, these techniques are usually expensive and do not allow rapid degradation prediction. Deep learning techniques such as convolutional neural networks and recurrent neural networks may be able to predict the phase boundary and/or defect pattern evolution under certain conditions after proper training 150 . Similar models can then be built to understand multiple degradation phenomena and aid the design of materials with improved cycle life.

Optimized smart power grids

A promising prospect of ML in smart grids is automating the decision-making processes that are associated with dynamic power supplies to distribute power most efficiently (Fig.  3e ). Practical deployment of ML technologies into physical systems remains difficult because of data scarcity and the risk-averse mindset of policymakers. The collection of and access to large amounts of diverse data is challenging owing to high cost, long delays and concerns over compliance and security 151 . For instance, to capture the variation of renewable resources owing to peak or off-peak and seasonal attributes, long-term data collections are implemented for periods of 24 hours to several years 152 . Furthermore, although ML algorithms are ideally supposed to account for all uncertainties and unpredictable situations in energy systems, the risk-adverse mindset in the energy management industry means that implementation still relies on human decision-making 153 .

An ML-based framework that involves a digital twin of the physical system can address these problems 154 , 155 . The digital twin represents the digitalized cyber models of the physical system and can be constructed from physical laws and/or ML models trained using data sampled from the physical system. This approach aims to accurately simulate the dynamics of the physical system, enabling relatively fast generation of large amounts of high-quality synthetic data at low cost. Notably, because ML model training and validation is performed on the digital twin, there is no risk to the actual physical system. Based on the prediction results, suitable actions can be suggested and then implemented in the physical system to ensure stability and/or improve system operation.

Policy optimization

Finally, research is generally focused on one narrow aspect of a larger problem; we argue that energy research needs a more integrated approach 156 (Fig.  3f ). Energy policy is the manner in which an entity, such as the government, addresses its energy issues, including conversion, distribution and utilization. ML has been used in the fields of energy economics finance for performance diagnostics (such as for oil wells), energy generation (such as wind power) and consumption (such as power load) forecasts and system lifespan (such as battery cell life) and failure (such as grid outage) prediction 157 . They have also been used for energy policy analysis and evaluation (for example, for estimating energy savings). A natural extension of ML models is to use them for policy optimization 158 , 159 , a concept that has not yet seen widespread use. We posit that the best energy policies — including the deployment of the newly discovered materials — can be improved and augmented with ML and should be discussed in research reporting accelerated energy technology platforms.

Conclusions

To summarize, ML has the potential to enable breakthroughs in the development and deployment of sustainable energy techniques. There have been remarkable achievements in many areas of energy technology, from materials design and device management to system deployment. ML is particularly well suited to discovering new materials, and researchers in the field are expecting ML to bring up new materials that may revolutionize the energy industry. The field is still nascent, but there is conclusive evidence that ML is at least able to expose the same trends that human researchers have noticed over decades of research. The ML field itself is still seeing rapid development, with new methodologies being reported daily. It will take time to develop and adopt these methodologies to solve specific problems in materials science. We believe that for ML to truly accelerate the deployment of sustainable energy, it should be deployed as a tool, similar to a synthesis procedure, characterization equipment or control apparatus. Researchers using ML to accelerate energy technology discovery should judge the success of the method primarily on the advances it enables. To this end, we have proposed the XPIs and some areas in which we hope to see ML deployed.

Davidson, D. J. Exnovating for a renewable energy transition. Nat. Energy 4 , 254–256 (2019).

Article   Google Scholar  

Horowitz, C. A. Paris agreement. Int. Leg. Mater. 55 , 740–755 (2016).

International Energy Agency 2018 World Energy Outlook: Executive Summary https://www.iea.org/reports/world-energy-outlook-2018 (OECD/IEA, 2018).

Chu, S., Cui, Y. & Liu, N. The path towards sustainable energy. Nat. Mater. 16 , 16–22 (2017).

Maine, E. & Garnsey, E. Commercializing generic technology: the case of advanced materials ventures. Res. Policy 35 , 375–393 (2006).

De Luna, P., Wei, J., Bengio, Y., Aspuru-Guzik, A. & Sargent, E. Use machine learning to find energy materials. Nature 552 , 23–27 (2017).

Wang, H., Lei, Z., Zhang, X., Zhou, B. & Peng, J. A review of deep learning for renewable energy forecasting. Energy Convers. Manag. 198 , 111799–111814 (2019).

Yao, Z. et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat. Mach. Intell. 3 , 76–86 (2021).

Rosen, A. S. et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter 4 , 1578–1597 (2021).

Article   CAS   Google Scholar  

Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349 , 255–260 (2015).

Personal, E., Guerrero, J. I., Garcia, A., Peña, M. & Leon, C. Key performance indicators: a useful tool to assess Smart Grid goals. Energy 76 , 976–988 (2014).

Helmus, J. & den Hoed, R. Key performance indicators of charging infrastructure. World Electr. Veh. J. 8 , 733–741 (2016).

Struck, M.-M. Vaccine R&D success rates and development times. Nat. Biotechnol. 14 , 591–593 (1996).

Moore, G. E. Cramming more components onto integrated circuits. Electronics 38 , 114–116 (1965).

Google Scholar  

Wetterstrand, K. A. DNA sequencing costs: data. NHGRI Genome Sequencing Program (GSP) www.genome.gov/sequencingcostsdata (2020).

Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380 , 1347–1358 (2019).

Jeong, J. et al. Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells. Nature 592 , 381–385 (2021).

NREL. Best research-cell efficiency chart. NREL https://www.nrel.gov/pv/cell-efficiency.html (2021).

Burger, B. et al. A mobile robotic chemist. Nature 583 , 237–241 (2020).

Clark, M. A. et al. Design, synthesis and selection of DNA-encoded small-molecule libraries. Nat. Chem. Biol. 5 , 647–654 (2009).

Pilania, G., Gubernatis, J. E. & Lookman, T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput. Mater. Sci. 129 , 156–163 (2017).

Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6 , 19375 (2016).

Lu, S. et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat. Commun. 9 , 3405 (2018).

Askerka, M. et al. Learning-in-templates enables accelerated discovery and synthesis of new stable double perovskites. J. Am. Chem. Soc. 141 , 3682–3690 (2019).

Jain, A. & Bligaard, T. Atomic-position independent descriptor for machine learning of material properties. Phys. Rev. B 98 , 214112 (2018).

Choubisa, H. et al. Crystal site feature embedding enables exploration of large chemical spaces. Matter 3 , 433–448 (2020).

Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120 , 145301–145306 (2018).

Broberg, D. et al. PyCDT: a Python toolkit for modeling point defects in semiconductors and insulators. Comput. Phys. Commun. 226 , 165–179 (2018).

Roch, L. M. et al. ChemOS: an orchestration software to democratize autonomous discovery. PLoS ONE 15 , 1–18 (2020).

Wei, L., Xu, X., Gurudayal, Bullock, J. & Ager, J. W. Machine learning optimization of p-type transparent conducting films. Chem. Mater. 31 , 7340–7350 (2019).

Schubert, M. F. et al. Design of multilayer antireflection coatings made from co-sputtered and low-refractive-index materials by genetic algorithm. Opt. Express 16 , 5290–5298 (2008).

Wang, C., Yu, S., Chen, W. & Sun, C. Highly efficient light-trapping structure design inspired by natural evolution. Sci. Rep. 3 , 1025 (2013).

Ripalda, J. M., Buencuerpo, J. & García, I. Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations. Nat. Commun. 9 , 5126 (2018).

Demant, M., Virtue, P., Kovvali, A., Yu, S. X. & Rein, S. Learning quality rating of As-Cut mc-Si wafers via convolutional regression networks. IEEE J. Photovolt. 9 , 1064–1072 (2019).

Musztyfaga-Staszuk, M. & Honysz, R. Application of artificial neural networks in modeling of manufactured front metallization contact resistance for silicon solar cells. Arch. Metall. Mater. 60 , 1673–1678 (2015).

Sendek, A. D. et al. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci. 10 , 306–320 (2017).

Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J. C. & Viswanathan, V. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent. Sci. 4 , 996–1006 (2018).

Zhang, Y. et al. Unsupervised discovery of solid-state lithium ion conductors. Nat. Commun. 10 , 5260 (2019).

Doan, H. A. et al. Quantum chemistry-informed active learning to accelerate the design and discovery of sustainable energy storage materials. Chem. Mater. 32 , 6338–6346 (2020).

Chemali, E., Kollmeyer, P. J., Preindl, M. & Emadi, A. State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach. J. Power Sources 400 , 242–255 (2018).

Richardson, R. R., Osborne, M. A. & Howey, D. A. Gaussian process regression for forecasting battery state of health. J. Power Sources 357 , 209–219 (2017).

Berecibar, M. et al. Online state of health estimation on NMC cells based on predictive analytics. J. Power Sources 320 , 239–250 (2016).

Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4 , 383–391 (2019).

Attia, P. M. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578 , 397–402 (2020).

Joshi, R. P. et al. Machine learning the voltage of electrode materials in metal-ion batteries. ACS Appl. Mater. Interf. 11 , 18494–18503 (2019).

Cubuk, E. D., Sendek, A. D. & Reed, E. J. Screening billions of candidates for solid lithium-ion conductors: a transfer learning approach for small data. J. Chem. Phys. 150 , 214701 (2019).

Sendek, A. D. et al. Machine learning-assisted discovery of solid Li-ion conducting materials. Chem. Mater. 31 , 342–352 (2019).

Kim, S., Jinich, A. & Aspuru-Guzik, A. MultiDK: a multiple descriptor multiple kernel approach for molecular discovery and its application to organic flow battery electrolytes. J. Chem. Inf. Model. 57 , 657–668 (2017).

Jinich, A., Sanchez-Lengeling, B., Ren, H., Harman, R. & Aspuru-Guzik, A. A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315000 redox reactions. ACS Cent. Sci. 5 , 1199–1210 (2019).

Sarkar, A. et al. High entropy oxides for reversible energy storage. Nat. Commun. 9 , 3400 (2018).

Choudhury, S. et al. Stabilizing polymer electrolytes in high-voltage lithium batteries. Nat. Commun. 10 , 3091 (2019).

Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361 , 360–365 (2018).

Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J. & Seh, Z. W. Predicting the state of charge and health of batteries using data-driven machine learning. Nat. Mach. Intell. 2 , 161–170 (2020).

Steinmann, S. N. & Seh, Z. W. Understanding electrified interfaces. Nat. Rev. Mater. 6 , 289–291 (2021).

Kandasamy, N., Badrinarayanan, R., Kanamarlapudi, V., Tseng, K. & Soong, B.-H. Performance analysis of machine-learning approaches for modeling the charging/discharging profiles of stationary battery systems with non-uniform cell aging. Batteries 3 , 18 (2017).

Wei, Q., Liu, D. & Shi, G. A novel dual iterative Q-learning method for optimal battery management in smart residential environments. IEEE Trans. Ind. Electron. 62 , 2509–2518 (2015).

Murphey, Y. L. et al. Intelligent hybrid vehicle power control — Part II: online intelligent energy management. IEEE Trans. Vehicular Technol. 62 , 69–79 (2013).

Seh, Z. W. et al. Combining theory and experiment in electrocatalysis: insights into materials design. Science 355 , eaad4998 (2017).

Staffell, I. et al. The role of hydrogen and fuel cells in the global energy system. Energy Environ. Sci. 12 , 463–491 (2019).

Montoya, J. H. H. et al. Materials for solar fuels and chemicals. Nat. Mater. 16 , 70–81 (2017).

Pérez-Ramírez, J. & López, N. Strategies to break linear scaling relationships. Nat. Catal. 2 , 971–976 (2019).

Shi, C., Hansen, H. A., Lausche, A. C. & Norskov, J. K. Trends in electrochemical CO 2 reduction activity for open and close-packed metal surfaces. Phys. Chem. Chem. Phys. 16 , 4720–4727 (2014).

Calle-Vallejo, F., Loffreda, D., Koper, M. T. M. & Sautet, P. Introducing structural sensitivity into adsorption-energy scaling relations by means of coordination numbers. Nat. Chem. 7 , 403–410 (2015).

Ulissi, Z. W. et al. Machine-learning methods enable exhaustive searches for active bimetallic facets and reveal active site motifs for CO 2 reduction. ACS Catal. 7 , 6600–6608 (2017).

Nørskov, J. K., Studt, F., Abild-Pedersen, F. & Bligaard, T. Activity and selectivity maps. In Fundamental Concepts in Heterogeneous Catalysis 97–113 (John Wiley, 2014).

Garijo del Río, E., Mortensen, J. J. & Jacobsen, K. W. Local Bayesian optimizer for atomic structures. Phys. Rev. B 100 , 104103 (2019).

Jørgensen, M. S., Larsen, U. F., Jacobsen, K. W. & Hammer, B. Exploration versus exploitation in global atomistic structure optimization. J. Phys. Chem. A 122 , 1504–1509 (2018).

Jacobsen, T. L., Jørgensen, M. S. & Hammer, B. On-the-fly machine learning of atomic potential in density functional theory structure optimization. Phys. Rev. Lett. 120 , 026102 (2018).

Peterson, A. A. Acceleration of saddle-point searches with machine learning. J. Chem. Phys. 145 , 074106 (2016).

Ulissi, Z. W., Medford, A. J., Bligaard, T. & Nørskov, J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8 , 14621 (2017).

Huang, Y., Chen, Y., Cheng, T., Wang, L.-W. & Goddard, W. A. Identification of the selective sites for electrochemical reduction of CO to C 2+ products on copper nanoparticles by combining reactive force fields, density functional theory, and machine learning. ACS Energy Lett. 3 , 2983–2988 (2018).

Chen, Y., Huang, Y., Cheng, T. & Goddard, W. A. Identifying active sites for CO 2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J. Am. Chem. Soc. 141 , 11651–11657 (2019).

Lai, Y., Jones, R. J. R., Wang, Y., Zhou, L. & Gregoire, J. M. Scanning electrochemical flow cell with online mass spectroscopy for accelerated screening of carbon dioxide reduction electrocatalysts. ACS Comb. Sci. 21 , 692–704 (2019).

Lai, Y. et al. The sensitivity of Cu for electrochemical carbon dioxide reduction to hydrocarbons as revealed by high throughput experiments. J. Mater. Chem. A 7 , 26785–26790 (2019).

Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO 2 reduction and H 2 evolution. Nat. Catal. 1 , 696–703 (2018).

Zhong, M. et al. Accelerated discovery of CO 2 electrocatalysts using active machine learning. Nature 581 , 178–183 (2020).

Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571 , 95–98 (2019).

Yao, Y. et al. Carbothermal shock synthesis of high-entropy-alloy nanoparticles. Science 359 , 1489–1494 (2018).

Yao, Y. et al. High-throughput, combinatorial synthesis of multimetallic nanoclusters. Proc. Natl Acad. Sci. USA 117 , 6316–6322 (2020).

Timoshenko, J. & Roldan Cuenya, B. In situ/operando electrocatalyst characterization by X-ray absorption spectroscopy. Chem. Rev. 121 , 882–961 (2021).

Zheng, C., Chen, C., Chen, Y. & Ong, S. P. Random forest models for accurate identification of coordination environments from X-ray absorption near-edge structure. Patterns 1 , 100013–100023 (2020).

Torrisi, S. B. et al. Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships. npj Comput. Mater. 6 , 109 (2020).

Timoshenko, J. et al. Neural network approach for characterizing structural transformations by X-ray absorption fine structure spectroscopy. Phys. Rev. Lett. 120 , 225502 (2018).

Marcella, N. et al. Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy. Phys. Chem. Chem. Phys. 22 , 18902–18910 (2020).

Chen, K., Laghrouche, S. & Djerdir, A. Degradation model of proton exchange membrane fuel cell based on a novel hybrid method. Appl. Energy 252 , 113439–113447 (2019).

Ma, R. et al. Data-driven proton exchange membrane fuel cell degradation predication through deep learning method. Appl. Energy 231 , 102–115 (2018).

Jeppesen, C. et al. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation. J. Power Sources 359 , 37–47 (2017).

Liu, J. et al. Sequence fault diagnosis for PEMFC water management subsystem using deep learning with t-SNE. IEEE Access. 7 , 92009–92019 (2019).

Ansari, M. A., Rizvi, S. M. A. & Khan, S. Optimization of electrochemical performance of a solid oxide fuel cell using artificial neural network. in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) 4230–4234 (IEEE, 2016).

Zhang, C. et al. Modelling of solid oxide electrolyser cell using extreme learning machine. Electrochim. Acta 251 , 137–144 (2017).

Esche, E., Weigert, J., Budiarto, T., Hoffmann, C. & Repke, J.-U. Optimization under uncertainty based on a data-driven model for a chloralkali electrolyzer cell. Computer-aided Chem. Eng. 46 , 577–582 (2019).

Siddaiah, R. & Saini, R. P. A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications. Renew. Sustain. Energy Rev. 58 , 376–396 (2016).

Yin, L., Yu, T., Zhang, X. & Yang, B. Relaxed deep learning for real-time economic generation dispatch and control with unified time scale. Energy 149 , 11–23 (2018).

Yu, T., Wang, H. Z., Zhou, B., Chan, K. W. & Tang, J. Multi-agent correlated equilibrium Q ( λ ) learning for coordinated smart generation control of interconnected power grids. IEEE Trans. Power Syst. 30 , 1669–1679 (2015).

Marino, D. L., Amarasinghe, K. & Manic, M. Building energy load forecasting using deep neural networks. in IECON Proceedings (Industrial Electronics Conference) 7046–7051 (IECON, 2016).

Ryu, S., Noh, J. & Kim, H. Deep neural network based demand side short term load forecasting. in 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm 2016) 308–313 (IEEE, 2016).

Mocanu, E., Nguyen, P. H., Kling, W. L. & Gibescu, M. Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning. Energy Build. 116 , 646–655 (2016).

Lund, P. D., Lindgren, J., Mikkola, J. & Salpakari, J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew. Sustain. Energy Rev. 45 , 785–807 (2015).

Kim, B. G., Zhang, Y., Van Der Schaar, M. & Lee, J. W. Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Trans. Smart Grid 7 , 2187–2198 (2016).

Dusparic, I., Taylor, A., Marinescu, A., Cahill, V. & Clarke, S. Maximizing renewable energy use with decentralized residential demand response. in 2015 IEEE 1st International Smart Cities Conference (ISC2 2015 ) 1–6 (IEEE, 2015).

Dusparic, I., Harris, C., Marinescu, A., Cahill, V. & Clarke, S. Multi-agent residential demand response based on load forecasting. in 2013 1st IEEE Conference on Technologies for Sustainability (SusTech 2013) 90–96 (IEEE, 2013).

Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 , 1798–1828 (2013).

Duvenaud, D. et al. Convolutional networks on graphs for learning molecular fingerprints. in Advances In Neural Information Processing Systems 2224–2232 (NIPS, 2015).

Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4 , 268–276 (2018).

Krenn, M., Hase, F., Nigam, A., Friederich, P. & Aspuru-Guzik, A. Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach. Learn. Sci. Technol. 1 , 045024–045031 (2020).

Jin, W., Barzilay, R. & Jaakkola, T. Junction tree variational autoencoder for molecular graph generation. Mach. Learn. 5 , 3632–3648 (2018).

You, J., Liu, B., Ying, R., Pande, V. & Leskovec, J. Graph convolutional policy network for goal-directed molecular graph generation. Adv. Neural Inf. Process. Syst. 31 , 6412–6422 (2018).

Liu, Q., Allamanis, M., Brockschmidt, M. & Gaunt, A. L. Constrained graph variational autoencoders for molecule design. in Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18) 7806–7815 (Curran Associates Inc., 2018).

Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87 , 184115 (2013).

Ward, L. et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Phys. Rev. B 96 , 024104 (2017).

Isayev, O. et al. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 8 , 15679 (2017).

Ziletti, A., Kumar, D., Scheffler, M. & Ghiringhelli, L. M. Insightful classification of crystal structures using deep learning. Nat. Commun. 9 , 2775 (2018).

Ryan, K., Lengyel, J. & Shatruk, M. Crystal structure prediction via deep learning. J. Am. Chem. Soc. 140 , 10158–10168 (2018).

Park, C. W. & Wolverton, C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys. Rev. Mater. 4 , 063801 (2020).

Liu, X. et al. Self-Supervised Learning: Generative or Contrastive (IEEE, 2020).

Ruder, S. An overview of multi-task learning in deep neural networks. Preprint at https://doi.org/10.48550/arXiv.1706.05098 (2017).

Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: a survey. IEEE Transactions on Pattern Analysis & Machine Intelligence 44 , 5149–5169 (2020).

Vaswani, A. et al. Attention is all you need. in Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17) 6000–6010 (Curran Associates Inc., 2017).

Veličković, P. et al. Graph attention networks. Preprint at https://doi.org/10.48550/arXiv.1710.10903 (2017).

Battaglia, P. W. et al. Relational inductive biases, deep learning, and graph networks. Preprint at https://doi.org/10.48550/arXiv.1806.01261 (2018).

Satorras, V. G., Hoogeboom, E. & Welling, M. E(n) equivariant graph neural networks. Preprint at https://doi.org/10.48550/arXiv.2102.09844 (2021).

Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. Apl. Mater. 1 , 011002–011012 (2013).

Laasner, R. et al. MatD3: a database and online presentation package for research data supporting materials discovery, design, and dissemination. J. Open Source Softw. 5 , 1945–1947 (2020).

Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365 , 557 (2019).

Hachmann, J. et al. The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2 , 2241–2251 (2011).

Bıyık, E., Wang, K., Anari, N. & Sadigh, D. Batch active learning using determinantal point processes. Preprint at https://doi.org/10.48550/arXiv.1906.07975 (2019).

Hoffmann, J. et al. Machine learning in a data-limited regime: augmenting experiments with synthetic data uncovers order in crumpled sheets. Sci. Adv. 5 , eaau6792 (2019).

Liu, J. Z. et al. Simple and principled uncertainty estimation with deterministic deep learning via distance awareness. Adv. Neural Inf. Process Syst. 33 , 7498–7512 (2020).

Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17) 6405–6416 (Curran Associates Inc., 2017).

D’Amour, A. et al. Underspecification presents challenges for credibility in modern machine learning. Preprint at https://doi.org/10.48550/arXiv.2011.03395 (2020).

Barredo Arrieta, A. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion. 58 , 82–115 (2020).

Ribeiro, M. T., Singh, S. & Guestrin, C. “Why should I trust you?” Explaining the predictions of any classifier. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (2016).

Lundberg, S. & Lee, S.-I. An unexpected unity among methods for interpreting model predictions. Preprint at https://arxiv.org/abs/1611.07478 (2016).

Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10 , e0130140 (2015).

Sun, W. et al. The thermodynamic scale of inorganic crystalline metastability. Sci. Adv. 2 , e1600225 (2016).

Aykol, M., Dwaraknath, S. S., Sun, W. & Persson, K. A. Thermodynamic limit for synthesis of metastable inorganic materials. Sci. Adv. 4 , eaaq0148 (2018).

Wei, J. N., Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci. 2 , 725–732 (2016).

Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10 , 370–377 (2019).

Nagabhushana, G. P., Shivaramaiah, R. & Navrotsky, A. Direct calorimetric verification of thermodynamic instability of lead halide hybrid perovskites. Proc. Natl Acad. Sci. USA 113 , 7717–7721 (2016).

Sanna, S. et al. Enhancement of the chemical stability in confined δ-Bi 2 O 3 . Nat. Mater. 14 , 500–504 (2015).

Podolyan, Y., Walters, M. A. & Karypis, G. Assessing synthetic accessibility of chemical compounds using machine learning methods. J. Chem. Inf. Model. 50 , 979–991 (2010).

Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. SCScore: synthetic complexity learned from a reaction corpus. J. Chem. Inf. Model. 58 , 252–261 (2018).

Kim, E. et al. Inorganic materials synthesis planning with literature-trained neural networks. J. Chem. Inf. Model. 60 , 1194–1201 (2020).

Huo, H. et al. Semi-supervised machine-learning classification of materials synthesis procedures. npj Comput. Mater. 5 , 62 (2019).

Tian, C., Lin, F. & Doeff, M. M. Electrochemical characteristics of layered transition metal oxide cathode materials for lithium ion batteries: surface, bulk behavior, and thermal properties. Acc. Chem. Res. 51 , 89–96 (2018).

Guilminot, E., Corcella, A., Charlot, F., Maillard, F. & Chatenet, M. Detection of Pt z + ions and Pt nanoparticles inside the membrane of a used PEMFC. J. Electrochem. Soc. 154 , B96 (2007).

Pender, J. P. et al. Electrode degradation in lithium-ion batteries. ACS Nano 14 , 1243–1295 (2020).

Li, Y. et al. Atomic structure of sensitive battery materials and interfaces revealed by cryo-electron microscopy. Science 358 , 506–510 (2017).

Wang, H. Numerical modeling of non-planar hydraulic fracture propagation in brittle and ductile rocks using XFEM with cohesive zone method. J. Pet. Sci. Eng. 135 , 127–140 (2015).

Hsu, Y.-C., Yu, C.-H. & Buehler, M. J. Using deep learning to predict fracture patterns in crystalline solids. Matter 3 , 197–211 (2020).

Wuest, T., Weimer, D., Irgens, C. & Thoben, K. D. Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4 , 23–45 (2016).

De Jong, P., Sánchez, A. S., Esquerre, K., Kalid, R. A. & Torres, E. A. Solar and wind energy production in relation to the electricity load curve and hydroelectricity in the northeast region of Brazil. Renew. Sustain. Energy Rev. 23 , 526–535 (2013).

Zolfani, S. H. & Saparauskas, J. New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Eng. Econ. 24 , 408–414 (2013).

Tao, F., Zhang, M., Liu, Y. & Nee, A. Y. C. Digital twin driven prognostics and health management for complex equipment. CIRP Ann. 67 , 169–172 (2018).

Yun, S., Park, J. H. & Kim, W. T. Data-centric middleware based digital twin platform for dependable cyber-physical systems. in International Conference on Ubiquitous and Future Networks (ICUFN) 922–926 (2017).

Boretti, A. Integration of solar thermal and photovoltaic, wind, and battery energy storage through AI in NEOM city. Energy AI 3 , 100038–100045 (2021).

Ghoddusi, H., Creamer, G. G. & Rafizadeh, N. Machine learning in energy economics and finance: a review. Energy Econ. 81 , 709–727 (2019).

Asensio, O. I., Mi, X. & Dharur, S. Using machine learning techniques to aid environmental policy analysis: a teaching case regarding big data and electric vehicle charging infrastructure. Case Stud. Environ. 4 , 961302 (2020).

Zheng, S., Trott, A., Srinivasa, S., Parkes, D. C. & Socher, R. The AI economist: taxation policy design via two-level deep multiagent reinforcement learning. Sci. Adv. 8 , eabk2607 (2022).

Sun, S. et al. A data fusion approach to optimize compositional stability of halide perovskites. Matter 4 , 1305–1322 (2021).

Sun, W. et al. Machine learning — assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials. Sci. Adv. 5 , eaay4275 (2019).

Sun, S. et al. Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis. Joule 3 , 1437–1451 (2019).

Kirman, J. et al. Machine-learning-accelerated perovskite crystallization. Matter 2 , 938–947 (2020).

Langner, S. et al. Beyond ternary OPV: high-throughput experimentation and self-driving laboratories optimize multicomponent systems. Adv. Mater. 32 , 1907801 (2020).

Hartono, N. T. P. et al. How machine learning can help select capping layers to suppress perovskite degradation. Nat. Commun. 11 , 4172 (2020).

Odabaşı, Ç. & Yıldırım, R. Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools. Nano Energy 56 , 770–791 (2019).

Fenning, D. P. et al. Darwin at high temperature: advancing solar cell material design using defect kinetics simulations and evolutionary optimization. Adv. Energy Mater. 4 , 1400459 (2014).

Allam, O., Cho, B. W., Kim, K. C. & Jang, S. S. Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries. RSC Adv. 8 , 39414–39420 (2018).

Okamoto, Y. & Kubo, Y. Ab initio calculations of the redox potentials of additives for lithium-ion batteries and their prediction through machine learning. ACS Omega 3 , 7868–7874 (2018).

Takagishi, Y., Yamanaka, T. & Yamaue, T. Machine learning approaches for designing mesoscale structure of Li-ion battery electrodes. Batteries 5 , 54 (2019).

Tan, Y., Liu, W. & Qiu, Q. Adaptive power management using reinforcement learning. in IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers (ICCAD ) 461–467 (IEEE, 2009).

Ermon, S., Xue, Y., Gomes, C. & Selman, B. Learning policies for battery usage optimization in electric vehicles. Mach. Learn. 92 , 177–194 (2013).

Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5 , 83 (2019).

Pilania, G. Machine learning in materials science: from explainable predictions to autonomous design. Comput. Mater. Sci. 193 , 110360 (2021).

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559 , 547–555 (2018).

Kaufmann, K. et al. Crystal symmetry determination in electron diffraction using machine learning. Science 367 , 564–568 (2020).

Chen, C., Zuo, Y., Ye, W., Li, X. & Ong, S. P. Learning properties of ordered and disordered materials from multi-fidelity data. Nat. Comput. Sci. 1 , 46–53 (2021).

Liu, M., Yan, K., Oztekin, B. & Ji, S. GraphEBM: molecular graph generation with energy-based models. Preprint at https://doi.org/10.48550/arXiv.2102.00546 (2021).

Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555 , 604–610 (2018).

Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559 , 377–381 (2018).

Epps, R. W. et al. Artificial chemist: an autonomous quantum dot synthesis bot. Adv. Mater. 32 , 2001626 (2020).

MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6 , eaaz8867 (2020).

Li, Z. et al. Robot-accelerated perovskite investigation and discovery. Chem. Mater. 32 , 5650–5663 (2020).

Aguiar, J. A., Gong, M. L., Unocic, R. R., Tasdizen, T. & Miller, B. D. Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning. Sci. Adv. 5 , eaaw1949 (2019).

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Acknowledgements

Z.Y. and A.A.-G. were supported as part of the Nanoporous Materials Genome Center by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DE-FG02-17ER16362 and the US Department of Energy, Office of Science — Chicago under award number DE-SC0019300. A.J. was financially supported by Huawei Technologies Canada and the Natural Sciences and Engineering Research Council (NSERC). L.M.M.-M. thanks the support of the Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program under cooperative agreement number HR00111920027 dated 1 August 2019. Y.W. acknowledges funding support from the Singapore National Research Foundation under its Green Buildings Innovation Cluster (GBIC award number NRF2015ENC-GBICRD001-012) administered by the Building and Construction Authority, its Green Data Centre Research (GDCR award number NRF2015ENC-GDCR01001-003) administered by the Info-communications Media Development Authority, and its Energy Programme (EP award number NRF2017EWT-EP003-023) administered by the Energy Market Authority of Singapore. A.A.-G. is a Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow. E.H.S. acknowledges funding by the Ontario Ministry of Colleges and Universities (grant ORF-RE08-034), the Natural Sciences and Engineering Research Council (NSERC) of Canada (grant RGPIN-2017-06477), the Canadian Institute for Advanced Research (CIFAR) (grant FS20-154 APPT.2378) and the University of Toronto Connaught Fund (grant GC 2012-13). Z.W.S. acknowledges funding by the Singapore National Research Foundation (NRF-NRFF2017-04).

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These authors contributed equally: Zhenpeng Yao, Yanwei Lum, Andrew Johnston.

Authors and Affiliations

Shanghai Key Laboratory of Hydrogen Science & Center of Hydrogen Science, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Zhenpeng Yao

Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

Zhenpeng Yao, Luis Martin Mejia-Mendoza & Alán Aspuru-Guzik

Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China

State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Innovis, Singapore, Singapore

Yanwei Lum & Zhi Wei Seh

Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada

Yanwei Lum, Andrew Johnston & Edward H. Sargent

School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

Xin Zhou & Yonggang Wen

Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

• Alán Aspuru-Guzik

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Z.Y., Y.L. and A.J. contributed equally to this work. All authors contributed to the writing and editing of the manuscript.

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Machine learning techniques that can query a user interactively to modify its current strategy (that is, label an input).

(AI). Theory and development of computer systems that exhibit intelligence.

A system for adjusting the power output of multiple generators at different power plants, in response to changes in the load.

A technology development pipeline that incorporates automation to go from idea to realization of technology. ‘Closed’ refers to the concept that the system improves with experience and iterations.

Process of increasing the amount of data through adding slightly modified copies or newly created synthetic data from existing data.

A generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables, with connections between the layers but not between units within each layer.

(DL). Machine learning subfield that is based on neural networks with representation learning.

The ability to adapt to new, unseen data, drawn from the same distribution as the one used to create the model.

Machine learning techniques that learn to model the data distribution of a dataset and sample new data points.

Degree to which a human can understand a model’s decision. Interpretability can be used to build trust and credibility.

A design method where new materials and compounds are ‘reverse-engineered’ simply by inputting a set of desired properties and characteristics and then using an optimization algorithm to generate a predicted solution.

A special kind of recurrent neural networks that are capable of selectively remembering patterns for a long duration of time.

(ML). Field within artificial intelligence that deals with learning algorithms, which improve automatically through experience (data).

A computerized system composed of multiple interacting intelligent agents.

The combination of ridge regression (a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated) with multiple kernel techniques.

Models that involve the analysis of multiple, simultaneous physical phenomena, which can include heat transfer, fluid flow, deformation, electromagnetics, acoustics and mass transport.

The field of solving problems that have important features at multiple scales of time and/or space.

A neural network is composed of parameterized and optimizable transformations.

A class of artificial neural networks where connections between nodes form a directed or undirected graph along a temporal sequence.

Process of incorporating additional information into the model to constrain its solution space.

Machine learning techniques that make a sequence of decisions to maximize a reward.

Features used in a representation learning model, which transforms inputs into new features for a task.

Technique for solving problems in the planning of chemical synthesis.

A robotic equipment automated chemical synthesis plan.

Design process composed of several stages where materials are iteratively filtered and ranked to arrive to a few top candidates.

Machine learning techniques that involve the usage of labelled data.

Machine learning techniques that adapt a learned representation or strategy from one dataset to another.

Process of evaluating the statistical confidence of model.

Machine learning techniques that learn patterns from unlabelled data.

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Yao, Z., Lum, Y., Johnston, A. et al. Machine learning for a sustainable energy future. Nat Rev Mater 8 , 202–215 (2023). https://doi.org/10.1038/s41578-022-00490-5

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Energy rising —

How new tech is making geothermal energy a more versatile power source, geothermal has moved beyond being confined to areas with volcanic activity..

Katarina Zimmer, Knowable Magazine - Apr 13, 2024 10:33 am UTC

Glistening in the dry expanses of the Nevada desert is an unusual kind of power plant that harnesses energy not from the sun or wind, but from the Earth itself.

Known as Project Red, it pumps water thousands of feet into the ground, down where rocks are hot enough to roast a turkey. Around the clock, the plant sucks the heated water back up to power generators. Since last November, this carbon-free, Earth-borne power has been flowing onto a local grid in Nevada.

Geothermal energy, though it’s continuously radiating from Earth’s super-hot core, has long been a relatively niche source of electricity, largely limited to volcanic regions like Iceland where hot springs bubble from the ground. But geothermal enthusiasts have dreamed of sourcing Earth power in places without such specific geological conditions—like Project Red’s Nevada site, developed by energy startup Fervo Energy.

Such next-generation geothermal systems have been in the works for decades, but they’ve proved expensive and technologically difficult, and have sometimes even triggered earthquakes. Some experts hope that newer efforts like Project Red may now, finally, signal a turning point, by leveraging techniques that were honed in oil and gas extraction to improve reliability and cost-efficiency.

The advances have garnered hopes that with enough time and money, geothermal power—which currently generates less than 1 percent of the world’s electricity , and 0.4 percent of electricity in the United States —could become a mainstream energy source. Some posit that geothermal could be a valuable tool in transitioning the energy system off of fossil fuels, because it can provide a continuous backup to intermittent energy sources like solar and wind . “It’s been, to me, the most promising energy source for a long time,” says energy engineer Roland Horne of Stanford University. “But now that we’re moving towards a carbon-free grid, geothermal is very important.”

A rocky start

Geothermal energy works best with two things: heat, plus rock that is permeable enough to carry water. In places where molten rock sizzles close to the surface, water will seep through porous volcanic rock, warm up and bubble upward as hot water, steam, or both.

If the water or steam is hot enough—ideally at least around 300 degrees Fahrenheit—it can be extracted from the ground and used to power generators for electricity. In Kenya, nearly 50 percent of electricity generated comes from geothermal. Iceland gets 25 percent of its electricity from this source, while New Zealand gets about 18 percent and the state of California, 6 percent.

Some natural geothermal resources are still untapped, such as in the western United States, says geologist Ann Robertson-Tait , president of GeothermEx, a geothermal energy consulting division at the oilfield services company SLB. But by and large, we’re running out of natural, high-quality geothermal resources, pushing experts to consider ways of extracting geothermal energy from areas where the energy is much harder to access. “There’s so much heat in the Earth,” Robertson-Tait says. But, she adds, “much of it is locked inside rock that isn’t permeable.”

Tapping that heat requires deep drilling and creating cracks in these non-volcanic, dense rocks to allow water to flow through them. Since 1970, engineers have been developing “enhanced geothermal systems” (EGS) that do just that, applying methods similar to the hydraulic fracturing—or fracking—used to suck oil and gas out of deep rocks. Water is pumped at high pressure into wells, up to several miles deep, to blast cracks into the rocks. The cracked rock and water create an underground radiator where water heats before rising to the surface through a second well. Dozens of such EGS installations have been built in the United States, Europe, Australia, and Japan—most of them experimental and government-funded—with mixed success.

Famously, one EGS plant in South Korea was abruptly shuttered in 2017 after having probably caused a 5.5-magnitude earthquake ; fracking of any kind can add pressure to nearby tectonic faults. Other issues were technological—some plants didn’t create enough fractures for good heat exchange, or fractures traveled in the wrong direction and failed to connect the two wells.

Some efforts, however, turned into viable power plants, including several German and French systems built between 1987 and 2012 in the Rhine Valley. There, engineers made use of existing fractures in the rock.

But overall, there just hasn’t been enough interest to develop EGS into a more reliable and lucrative technology, says geophysicist Dimitra Teza of the energy research institute Fraunhofer IEG in Karlsruhe, Germany, who helped develop some of the Rhine Valley EGS systems. “It has been quite tough for the industry.”

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116 Renewable Energy Essay Topics

🏆 best essay topics on renewable energy, 🌶️ hot renewable energy essay topics, 👍 good renewable energy research topics & essay examples, 💡 simple renewable energy essay ideas, ❓ renewable energy research questions.

• How Wind Turbines Convert Wind Energy into Electrical Energy?
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• Renewable Energy Sources: Popularity and Benefits Renewable fuels are not as pollutive as fossil fuels; they can be reproduced quickly from domestic resources. They became popular because of the decreasing amount of fossil fuels.
• Renewable Energy Usage: Advantages and Disadvantages This treatise attempts to support the statement that there are both advantages and disadvantages to the use of renewable energy with focus on hydroelectric power.
• Wind Energy as an Alternative Source While energy is a must for our survival, wind energy as a seemingly perpetual source of energy is the potential answer to the energy security of our generations to come.
• Renewable Energy in Japan: Clean Energy Transition Renewable energy in Japan became significantly important after the Fukushima Daiichi tsunami that struck Japan in 2011.
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• Discussion of Realization of Solar Energy Company ABC is interested in creating a “solar” project which will fully install and staff solar panels to ensure the safe transformation of solar energy into electricity.
• Renewable Energy Sources: Definition, Types and Stocks This research report analyzes the growing interest of the use renewable energy as an alternative to the non-renewable energy.
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• The Concept of Sustainability in Energy Plan for 2030-2040 The paper discusses the concept of sustainability takes a central role in the global discussion and presents of environment safety plan.
• The G20 Countries’ Competitiveness in Renewable Energy Resources “Assessing national renewable energy competitiveness of the G20” by Fang et al. presents an assessment of competitiveness in renewable energy resources among G20 countries.
• Future of 100% Renewable Energy This article explores the future of renewable green energy and a review the topical studies related to 100% renewable energy.
• Full Renewable Energy Plan Feasibility for 2030-2040 This paper argues that green energy in its current state will struggle to meet humanity’s demand and the development of better hybrid, integrated grids is required.
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• A World With 100% Renewable Energy Large corporations, countries, and separate states have already transferred or put a plan into action to transfer to 100% renewable energy in a couple of decades.
• Renewable Energy: Why Do We Need It? Renewable sources of energy such as solar, wind, or hydropower can bring multiple environmental benefits and tackle the problems of climate change and pollution in several ways.
• Renewable Energy Programs in Five Countries Energy production is vital for the drive of the economy. The world at large should diversify the sources to reduce the over-usage of fossil energy that is a threat of depletion.
• Wind Works Ltd.: Wind Energy Development Methodology Wind Works Ltd, as the company, which provides the alternative energy sources, and makes them available for the wide range of the population needs to resort to a particular assessment strategies.
• Solar Power as the Best Source of Energy The concepts of environmental conservation and sustainability have forced many countries and organizations to consider the best strategies or processes for generating electricity.
• Installing Solar Panels to Reduce Energy Costs The purpose of the proposal is to request permission for research to install solar panels to reduce energy costs, which represent a huge part of the company’s expenses.
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• Sunburst Renewable Energy Corporation: Business Structuring The proposed Sunburst Renewable Energy Corporation will function on a captivating value statement in product strategy and customer relationships as the core instruments of sustainable operations.
• Renewable Energy: Economic and Health Benefits The US should consider the adoption of renewable sources of energy, because of the high cost of using fossil fuels and expenses related to health problems due to pollution.
• Renewable Energy Systems Group and Toyota Company The application of the Lean Six Sigma to the key company processes, creates prerequisites for stellar success, as the examples of Toyota and the Renewable Energy Systems Group have shown.
• Renewable Energy Systems: Australia’s Electricity
• Accelerating Renewable Energy Electrification and Rural Economic Development With an Innovative Business Model
• Renewable Energy Systems: Role of Grid Connection
• Breaking Barriers Towards Investment in Renewable Energy
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• Marine Renewable Energy Clustering in the Mediterranean Sea: The Case of the PELAGOS Project
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• Addressing the Renewable Energy Financing Gap in Africa to Promote Universal Energy Access: Integrated Renewable Energy Financing in Malawi
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• Between Innovation and Industrial Policy: How Washington Succeeds and Fails at Renewable Energy
• Increasing Financial Incentive for Renewable Energy in the Third World
• Does Financial Development Matter for Innovation in Renewable Energy?
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• Low-Carbon Transition: Private Sector Investment in Renewable Energy Projects in Developing Countries
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• China’s Renewable Energy Policy: Commitments and Challenges
• Analyzing the Dynamic Impact of Electricity Futures on Revenue and Risk of Renewable Energy in China
• Driving Energy: The Enactment and Ambitiousness of State Renewable Energy Policy
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Rock permeability, microquakes link may be a boon for geothermal energy

Researchers report the strength of seismic activity has a direct link to energy extraction efficiency.

Using machine learning, researchers at Penn State have tied low-magnitude microearthquakes to the permeability of subsurface rocks beneath the Earth, a discovery that could have implications for improving geothermal energy transfer.

Generating geothermal energy requires a permeable subsurface to efficiently release heat when cold fluids are forced into the rock. This research reveals the optimum times for efficient energy transfer by exposing the link to microearthquakes, which are monitored on the surface through seismometers. The team published their findings in Nature Communications .

Using funding from the U.S. Department of Energy (DOE) and two datasets from the EGS Collab and Utah FORGE demonstration projects, researchers used machine learning to extract the "noise" found in the data that obscured the link. Researchers then used machine learning to create a model from one site and successfully applied it to the other -- a process called transfer learning -- suggesting that the link was formed based on general physics of subsurface rocks. That means it's likely to be universally true for all geothermal energy sites, the researchers said.

"Success of transfer learning confirms the generalizability of the models," said Pengliang Yu, postdoctoral scholar at Penn State and lead author of the study. "This suggests seismic monitoring could broadly be used to improve geothermal energy transfer efficiencies across a wide range of sites."

Increasing rock permeability is critical to a range of energy extraction methods, Yu said. Rock permeability impacts traditional fossil fuel recovery as well as renewable energies including hydrogen production. Hydrofracturing methods introduce cold fluids into the subsurface through porous rock, which creates high pressures that break the rock in tension or shear. This process creates microearthquakes similar to naturally occurring earthquakes, but at a much smaller scale. By increasing the permeability of the rock, energies such as heat and hydrocarbons are able to more easily reach the surface.

Yu said their algorithm showed a direct link, meaning the rock became the most permeable when the seismic activity was strongest.

Identifying the link between seismic activity and rock permeability improves the ability to extract energy while ensuring microquakes stay below the threshold that could cause damage or be observed by the public.

"Machine learning played a key role in uncovering the relationship between seismic activity and rock permeability" said co-author Parisa Shokouhi, professor of engineering science and mechanics in the College of Engineering. "It helped identify the important attributes of the seismic data for predicting rock permeability evolution. We constrained the machine learning algorithm to ensure a physically meaningful model. In return, the model prediction revealed a previously unknown physical link between seismic data and rock permeability."

Increasing the availability of geothermal energy would lessen dependence on fossil fuels, the researchers said. Additionally, they noted that linking rock permeability to microquakes can be useful in monitoring gas movement for carbon sequestration and the production and storage of subsurface hydrogen.

The research is part of a larger DOE-funded project to decrease the cost and increase production of geothermal energy and use machine learning to better understand and predict earthquakes, including microquakes.

"Yu's work is part of our effort to advance geothermal exploration and geothermal energy production using machine learning methods, said co-author Chris Marone, professor of geosciences at Penn State. "Our lab studies show clear connections between the evolution of elastic properties prior to lab earthquakes, and we are excited to see that similar relationships are observed in nature."

Ankur Mali, Department of Computer Science & Engineering at the University of South Florida and graduate of Penn State; Thejasvi Velaga, research assistant in the Department of Computer Science and Engineering at Penn State; Alex Bi, an undergraduate student majoring in health policy and administration at Penn State. Jiayi Yu, graduate student in the Department of Geosciences at Penn State; and Derek Elsworth, G. Albert Shoemaker Chair and professor of energy and mineral engineering and geosciences at Penn State, contributed to this research.

• Energy Technology
• Energy and Resources
• Thermodynamics
• Energy and the Environment
• Renewable Energy
• Earthquakes
• Environmental Science
• Wind turbine
• Geothermal power
• Renewable energy
• Climate change mitigation
• Precambrian

Story Source:

Materials provided by Penn State . Original written by David Kubarek. Note: Content may be edited for style and length.

Journal Reference :

• Pengliang Yu, Ankur Mali, Thejasvi Velaga, Alex Bi, Jiayi Yu, Chris Marone, Parisa Shokouhi, Derek Elsworth. Crustal permeability generated through microearthquakes is constrained by seismic moment . Nature Communications , 2024; 15 (1) DOI: 10.1038/s41467-024-46238-3

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Office: Bioenergy Technologies Office (BETO) & Office of Fossil Energy and Carbon Management (FECM)

FOA number:  DE-FOA-0003274

FOA Amount: $18.8 million

Phase 1 Selections to be Announced : ~September 2024

Link to Apply:  MACRO: Mixed Algae Conversion Research Opportunity

Today, the U.S. Department of Energy’s  Bioenergy Technologies Office (BETO) and  Office of Fossil Energy and Carbon Management (FECM) announced the MACRO: Mixed Algae Conversion Research Opportunity   funding opportunity announcement (FOA).   The FOA will award up to $18.8 million to address research and development (R&D) challenges in converting algae, such as seaweeds and other wet waste feedstocks, to biofuels and bioproducts that can decarbonize domestic transportation, industry, and communities.

Seaweeds, also known as macroalgae, are an emerging biomass resource with unique benefits compared to land-based biomass systems. However, they are underutilized and are difficult to convert due to their variability, unique chemical make-up, and storage instability. Overcoming these conversion challenges will help build algae biomass supply chains, accelerate their demand, and ultimately, drive the U.S. bioeconomy by enabling greater volumes of  sustainable aviation fuel (SAF) and carbon dioxide (CO 2 ) conversion to algae.

A wide range of biomass resources, such as readily available seaweeds and wet waste feedstocks, will be critical to reach the  Biden-Harris administration’s decarbonization goal of an equitable, clean energy future of net-zero emissions, economy-wide by no later than 2050. This FOA will also support the government-wide targets of the Sustainable Aviation Fuel Grand Challenge , the Executive Order on Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure American Bioeconomy , and the Clean Fuels & Products Shot™ .

Topic Areas

Aligning with BETO and FECM’s strategic program goals, the FOA will fund selected projects in two focus areas: 

• Topic Area 1 : Conversion of Seaweeds to Low Carbon Fuels and Bioproducts   This Topic Area, funded by BETO, will focus on laboratory scale R&D on conversion of seaweeds and seaweed blends with other wet wastes to renewable fuels and bioproducts to enable these readily available feedstocks to access new markets. It aims to address gaps in storage, mobilization, and conversion of readily available algae, including offshore farmed seaweeds, seaweed wastes, and blends of seaweed with other waste algae or blends of seaweed with other wet wastes to low-carbon fuels and bioproducts to enable these readily available feedstocks to access new markets. If successful, these efforts will enable market expansion, address community waste management challenges, and reduce greenhouse gas reductions.  
• Topic Area 2 : Conversion of Algal Biomass for Low Carbon Agricultural Bioproducts This Topic Area, funded by FECM, will support the use of anthropogenic CO 2 streams from industrial sources or utilities to grow micro- and macroalgae for low-carbon bioproducts. It aims to utilize CO 2 emissions streams from utilities or industrial sources to grow algae for source material and create value-added bioproducts (exclusive of fuels). Applications are sought that utilize anthropogenic (e.g., fossil fuel derived) CO 2 emissions, including concentrated CO 2 supplied from DAC technologies, in the cultivation process and then convert macro- and/or micro-algae into low-carbon agricultural applications or bioproducts such as animal feed. Applications are encouraged to focus on optimization of the technologies and processes for the conversion of cultivated algae biomass to bioproducts and clearly describe the end use bioproducts targeted.

Both topic areas contribute to BETO’s  strategic goals for SAFs and other low-carbon bioproducts , as well as FECM’s aims to use CO 2   emissions to grow algae and convert these feedstocks into low-carbon agricultural bioproducts.

BETO anticipates making approximately five to six financial assistance awards lasting from 24 to 36 months under this FOA. FECM intends to award three to four financial assistance awards that will run up to 24 months in length.  

The FOA concept paper  deadline is 5:00 p.m. ET, on May 10, 2024 , and full applications are  due at 5:00 p.m. ET, on June 27, 2024 . More information on the MACRO FOA and how to apply can be found on EERE Exchange . Additional information on the FOA and applicant eligibility is also available on Grants.gov .

An informational webinar  for potential applicants will be held on Wednesday, April 17, 2024, at 1:00 p.m. ET . 

Additional Information

Visit  BETO’s funding announcement page  for other upcoming funding opportunities.

Teaming Partner List

BETO has compiled a  Teaming Partner List  to encourage collaboration and facilitate the formation of project teams. The list allows organizations wishing to apply for funding to explore partnering with other prospective applicants. Partnerships can provide additional expertise and resources to successfully meet FOA goals. 

Please include the following information in your partner list submission:

Topic Field -  Please enter the Subtopic Area you are interested in. If you would like to be included in multiple Subtopic Areas, please complete separate submissions.

Background, Interests, Capabilities Field -  Please include information about your organization type/characteristics in the “Background, Interest, Capabilities” field to help other participants identify your expertise and resources. BETO also seeks applications from diverse teams of minority-serving institutions (MSIs), including Historically Black Colleges and Universities (HBCUs), Minority Business Enterprises, Minority-Owned Businesses, Woman-Owned Businesses, Veteran-Owned Businesses, and tribal entities; workforce education and training providers; and labor organizations.

Any organization that would like to be included on this list should submit the required information via the  Teaming Partner List  on EERE Exchange.

Disclaimer:  By submitting a request to be included on the Teaming Partner List, the requesting organization consents to the publication of the above-referenced information. By enabling and publishing the Teaming Partner List, EERE is not endorsing, sponsoring, or otherwise evaluating the qualifications of the individuals and organizations that are self-identifying themselves for placement on this Teaming Partner List. EERE will not pay for the provision of any information, nor will it compensate any applicants or requesting organizations for the development of such information.

Technology Verification

All applications selected for award negotiations under Topic Area 1 of this FOA are required to participate in a verification process led by DOE’s identified external third-party non-conflicted verification team. This verification process provides  technical assistance  to both BETO and the project by providing an in-depth analysis of key technical and economic metrics to ensure transparency and increase the likelihood of project success.

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A Tantalizing ‘Hint’ That Astronomers Got Dark Energy All Wrong

Scientists may have discovered a major flaw in their understanding of that mysterious cosmic force. That could be good news for the fate of the universe.

An interactive flight through millions of galaxies mapped using coordinate data from the Dark Energy Spectroscopic Instrument, or DESI. Credit... By Fiske Planetarium, University Of Colorado Boulder And Desi Collaboration

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By Dennis Overbye

• April 4, 2024

On Thursday, astronomers who are conducting what they describe as the biggest and most precise survey yet of the history of the universe announced that they might have discovered a major flaw in their understanding of dark energy, the mysterious force that is speeding up the expansion of the cosmos.

Dark energy was assumed to be a constant force in the universe, both currently and throughout cosmic history. But the new data suggest that it may be more changeable, growing stronger or weaker over time, reversing or even fading away.

“As Biden would say, it’s a B.F.D.,” said Adam Riess, an astronomer at Johns Hopkins University and the Space Telescope Science Institute in Baltimore. He shared the 2011 Nobel Prize in Physics with two other astronomers for the discovery of dark energy, but was not involved in this new study. “It may be the first real clue we have gotten about the nature of dark energy in 25 years,” he said.

That conclusion, if confirmed, could liberate astronomers — and the rest of us — from a longstanding, grim prediction about the ultimate fate of the universe. If the work of dark energy were constant over time, it would eventually push all the stars and galaxies so far apart that even atoms could be torn asunder, sapping the universe of all life, light, energy and thought, and condemning it to an everlasting case of the cosmic blahs. Instead, it seems, dark energy is capable of changing course and pointing the cosmos toward a richer future.

The key words are “might” and “could.” The new finding has about a one-in-400 chance of being a statistical fluke, a degree of uncertainty called three sigma, which is far short of the gold standard for a discovery, called five sigma: one chance in 1.7 million. In the history of physics, even five-sigma events have evaporated when more data or better interpretations of the data emerged.

This news comes in the first progress report, published as a series of papers, by a large international collaboration called the Dark Energy Spectroscopic Instrument, or DESI. The group has just begun a five-year effort to create a three-dimensional map of the positions and velocities of 40 million galaxies across 11 billion years of cosmic time. Its initial map, based on the first year of observations, includes just six million galaxies. The results were released today at a meeting of the American Physical Society in Sacramento, Calif., and at the Rencontres de Moriond conference in Italy.

“So far we’re seeing basic agreement with our best model of the universe, but we’re also seeing some potentially interesting differences that could indicate that dark energy is evolving with time,” Michael Levi, the director of DESI, said in a statement issued by the Lawrence Berkeley National Laboratory, which manages the project.

The DESI team had not expected to hit pay dirt so soon, Nathalie Palanque-Delabrouille, an astrophysicist at the Lawrence Berkeley lab and a spokeswoman for the project, said in an interview. The first year of results was designed to simply confirm what was already known, she said: “We thought that we would basically validate the standard model.”

But the unknown leaped out at them.

When the scientists combined their map with other cosmological data, they were surprised to find that it did not quite agree with the otherwise reliable standard model of the universe, which assumes that dark energy is constant and unchanging. A varying dark energy fit the data points better.

“It’s certainly more than a curiosity,” Dr. Palanque-Delabrouille said. “I would call it a hint. Yeah, it’s not yet evidence, but it’s interesting.”

But cosmologists are taking this hint very seriously.

Wendy Freedman, an astrophysicist at the University of Chicago who has led efforts to measure the expansion of the universe, praised the new survey as “superb data.” The results, she said, “open the potential for a new window into understanding dark energy, the dominant component of the universe, which remains the biggest mystery in cosmology. Pretty exciting.”

Michael Turner, an emeritus professor at the University of Chicago who coined the term “dark energy,” said in an email: “While combining data sets is tricky, and these are early results from DESI, the possible evidence that dark energy is not constant is the best news I have heard since cosmic acceleration was firmly established 20-plus years ago.”

Dark energy entered the conversation in 1998, when two competing groups of astronomers, including Dr. Riess, discovered that the expansion of the universe was speeding up rather than slowing, as most astronomers had expected. The initial observations seemed to suggest that this dark energy was acting just like a famous fudge factor — denoted by the Greek letter Lambda — that Einstein had inserted into his equations to explain why the universe didn’t collapse from its own gravity. He later called it his worst blunder.

But perhaps he spoke too soon. As formulated by Einstein, Lambda was a property of space-itself: The more space there was as the universe expanded, the more dark energy there was, pushing ever harder and eventually leading to a runaway, lightless future.

Dark energy took its place in the standard model of the universe known as L.C.D.M., composed of 70 percent dark energy (Lambda), 25 percent cold dark matter (an assortment of slow-moving exotic particles) and 5 percent atomic matter. So far that model has been bruised but not broken by the new James Webb Space Telescope. But what if dark energy were not constant as the cosmological model assumed?

At issue is a parameter called w , which is a measure of the density, or vehemence, of the dark energy. In Einstein’s version of dark energy, this number remains constant, with a value of –1, throughout the life of the universe. Cosmologists have been using this value in their models for the past 25 years.

But this version of dark energy is merely the simplest one. “With DESI we now have achieved a precision that allows us to go beyond that simple model,” Dr. Palanque-Delabrouille said, “to see if the density of dark energy is constant over time, or if it has some fluctuations and evolution with time.”

The DESI project, 14 years in the making, was designed to test the constancy of dark energy by measuring how fast the universe was expanding at various times in the past. To do that, scientists outfitted a telescope at Kitt Peak National Observatory with 5,000 fiber-optic detectors that could conduct spectroscopy on that many galaxies simultaneously and find out how fast they were moving away from Earth.

As a measure of distance, the researchers used bumps in the cosmic distribution of galaxies, known as baryon acoustic oscillations. These bumps were imprinted on the cosmos by sound waves in the hot plasma that filled the universe when it was just 380,000 years old. Back then, the bumps were a half-million light-years across. Now, 13.5 billion years later, the universe has expanded a thousandfold, and the bumps — which are now 500 million light-years across — serve as convenient cosmic measuring sticks.

The DESI scientists divided the past 11 billion years of cosmic history into seven spans of time. (The universe is 13.8 billion years old.) For each, they measured the size of these bumps and how fast the galaxies in them were speeding away from us and from each other.

When the researchers put it all together, they found that the usual assumption — a constant dark energy — didn’t work to describe the expansion of the universe. Galaxies in the three most recent epochs appeared closer than they should have been, suggesting that dark energy could be evolving with time.

“And we do see, indeed, a hint that the properties of dark energy would not correspond to a simple cosmological constant” but instead may “have some deviations,” Dr. Palanque-Delabrouille said. “And this is the first time we have that.” But, she emphasized again, “I wouldn’t call it evidence yet. It’s too, too weak.”

Time and more data will tell the fate of dark energy, and of cosmologists’ battle-tested model of the universe

“L.C.D.M. is being put through its paces by precision tests coming at it from every direction,” Dr. Turner said. “And it is doing well. But, when everything is taken together, it is beginning to appear that something isn’t right or something is missing. Things don’t fit together perfectly. And DESI is the latest indication.”

Dr. Riess of Johns Hopkins, who had an early look at the DESI results, noted that the “hint,” if validated, could pull the rug out from other cosmological measurements, such as the age or size of the universe. “This result is very interesting and we should take it seriously,” he wrote in his email. “Otherwise why else do we do these experiments?”

Dennis Overbye is the cosmic affairs correspondent for The Times, covering physics and astronomy. More about Dennis Overbye

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Never miss an eclipse, a meteor shower, a rocket launch or any other 2024 event  that’s out of this world with  our space and astronomy calendar .

Scientists may have discovered a major flaw in their understanding of dark energy, a mysterious cosmic force . That could be good news for the fate of the universe.

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1. what americans think about an energy transition from fossil fuels to renewables.

Most Americans think the U.S. should prioritize the development of renewable energy over fossil fuel sources. At the same time, most say they are not ready to stop using fossil fuel energy sources altogether. And a sizable share think the U.S. should never stop using fossil fuel sources.

Renewable sources, such as wind and solar, are expected to make up a growing share of the U.S. energy supply relative to fossil fuel sources such as oil, coal and natural gas in coming years. Last year renewable energy sources, including wind, solar and hydropower, generated more electricity than coal in the U.S. Legislation passed during the Biden administration, such as the Inflation Reduction Act, are expected to increase the pace of an energy transition .

In the new survey, 67% of Americans say the U.S. should prioritize developing alternative energy sources, such as wind, solar and hydrogen technology, while 32% say the priority should be expanding the exploration and production of oil, coal and natural gas.

Read the Appendix for more on this question, including a shift away from renewables among Republicans that occurred at the outset of the Biden administration .

While the public prioritizes renewable energy development, just 31% say they are ready to phase out the use of oil, coal and natural gas completely. A much larger share (68%) say the U.S. should continue to use fossil fuels, alongside renewables, as part of the mix of energy sources the country relies on.

The roughly two-thirds of Americans who support using a mix of renewables and fossil fuels are closely divided over whether the U.S. should ever stop using oil, coal and natural gas: 32% of Americans favor a mix of sources now but think the U.S. should eventually stop using fossil fuel energy sources, while 35% favor using a mix of sources now and say the U.S. should never stop using oil, coal and natural gas.

Republicans and Democrats offer very different views on what role oil, coal and natural gas should play in the country’s energy landscape.

An overwhelming majority of Republicans and Republican leaners (87%) think the U.S. should use a mix of fossil fuel and renewable energy sources. Looking ahead, 57% of Republicans believe the U.S. should never stop using oil, coal, and natural gas.

In contrast, most Democrats and Democratic leaners think the U.S. should end the use of fossil fuels, but there are some differences in views over the timeline.

About half of Democrats (48%) are ready to phase out fossil fuels now; another 35% think they should be part of the mix currently, but that the country should eventually stop using them. A relatively small share of Democrats (15%) say the country should never stop using oil, coal and natural gas.

Public expectations on how a renewable energy transition would impact the country

Americans think a major shift from fossil fuels to renewable energy sources in the U.S. would come with some difficulties for the country. But they also see potential benefits, such as improved air and water quality and a more positive than negative impact on jobs in the energy sector.

Still, many worry that an energy transition would push consumer prices higher. As with views on energy sources generally, Republicans and Democrats have quite different expectations for what a renewable energy transition would bring for the U.S.

Overall, 34% say the country would be very likely to encounter unexpected problems if the U.S. greatly reduces energy production from fossil fuels while increasing production from renewable sources; another 38% say unexpected problems would be somewhat likely.

Among Republicans, a majority (57%) think an energy transition to renewables would very likely lead to unexpected problems and 31% say this would make unexpected problems somewhat likely. A majority of Democrats also think unexpected problems from an energy transition would be at least somewhat likely (60%), but just 14% consider this very likely.

The new survey finds the public has mixed views about the likely effect of an energy transition on the country’s energy independence: 43% think an energy transition would make U.S. independence from foreign energy sources easier and 40% say it would be harder.

A 2022 Center survey found a majority of Americans said reducing U.S. dependence on foreign energy sources should be a top priority for the country’s energy policies.

A 62% majority of Republicans think that energy independence from other countries would be harder with a major shift to reliance on renewable energy sources. By contrast, 62% of Democrats think that energy independence would be easier.

The Center survey asked about a number of potential consequences from shifting away from fossil fuels and toward renewable energy sources.

Americans are most optimistic about how an energy transition would impact environmental quality: 59% think that air and water quality would get better if the U.S. greatly reduced fossil fuel energy production and increased production from renewable sources. Three-in-ten say this energy transition would not have much effect on air and water quality, while 11% say it would make air and water quality worse.

On balance, more Americans think a renewable energy transition would make local job opportunities in the energy sector better (49%) than worse (25%).

Concerns are more pronounced when it comes to prices. Slightly more Americans think an energy transition would make the prices they pay to heat and cool their homes worse (42%) than better (37%). And by a wider margin (44% to 25%) Americans think such a transition would make prices for everyday goods worse than better.

Democrats have a largely positive outlook on what an energy transition would mean for the country. Majorities think it would improve air and water quality (80%) and local job prospects in the energy sector (68%). And, on balance, more think it would help than hurt the frequency of extreme weather events, prices to heat and cool homes, and the reliability of the electrical grid.

Still, Democrats do have some reservations about the impact on consumer prices: 40% think a major shift toward renewable energy would make prices for consumer goods worse, compared with 35% who say this shift would make prices better.

Republicans are largely pessimistic about the likely effects of an energy transition.

Two-thirds of Republicans (66%) think consumer prices would get worse, as would home heating and cooling costs (65%). Roughly six-in-ten Republicans (59%) expect the reliability of the electric grid to go down.

And there’s skepticism toward environmental benefits: 48% say a transition would not have much effect on air and water quality, and 70% think there would be no effect on the frequency of extreme weather events.

Republican support for increasing wind and solar power, while still a majority, continues to edge downward

Americans remain broadly in favor of expanding solar and wind power: 82% of U.S. adults favor more solar panel farms and 75% favor more wind turbine farms. A smaller majority of Americans (57%) favor expanding nuclear power. Support for expanding other energy sources is lower: Fewer than half support more offshore oil and gas drilling (47%), hydraulic fracturing (44%) or coal mining (38%). Refer to the Appendix for details.

Democrats and Republicans remain deeply divided over fossil fuel development. There’s more common ground when it comes to renewables, though the partisan gap over expanding wind and solar power has been widening.

As of 2020, overwhelming majorities of both Democrats and Republicans were in favor of more solar and wind power. Since that time, Republican support has declined.

In the new survey, 93% of Democrats say they favor more solar panel farms, compared with 70% of Republicans. The share of Republicans who favor more solar power is down 14 percentage points since 2020 and 7 points since the survey last year.

Six-in-ten Republicans favor more wind power in the new survey, down 15 points since 2020 and 4 points in the last year. Meanwhile, 91% of Democrats favor more wind turbine farms.

The drop in support for solar and wind power has been especially pronounced among conservative Republicans. The share of conservative Republicans who favor more solar power is down 17 points from 2020 (from 80% to 63%) and the share who favor more wind power is down 18 points (from 69% to 51%) over the same time period.

Partisan differences remain wide over support for increasing fossil fuel energy sources. For example, 73% of Republicans favor more offshore oil and gas drilling, while just 25% of Democrats favor this.

There are similarly wide partisan differences over expanding hydraulic fracturing for oil and natural gas (68% of Republicans favor vs. 23% of Democrats) and coal mining (63% of Republicans vs. 16% of Democrats).

Republicans’ levels of support for more hydraulic fracturing, offshore drilling and coal mining have all moved about 10 points higher since 2020.

Republicans remain more likely to favor expanding nuclear power plants than Democrats (67% vs. 50%). Support for nuclear power has increased in both party coalitions in recent years. Men also continue to be far more likely than women to favor expanding nuclear power plants (71% vs. 44%).

A majority of Republicans who have heard of the Willow oil drilling project favor the Biden administration decision, while Democrats aware of the project oppose it

In March, the Biden administration approved the Willow oil drilling project on lands located in the federally owned National Petroleum Reserve in Alaska.

While the approval drew wide criticism from climate activists , the Center survey finds limited familiarity with the project. Just 3% of Americans say they have heard or read a lot about it and 29% say they have heard or read a little. About seven-in-ten (68%) say they have heard or read nothing at all about the Biden administration approving the Willow project.

Among those with at least a little awareness of this, 44% say they favor the Biden administration approving the drilling project, while 41% oppose it. The remaining 14% say they are not sure of their view on this.

In contrast to public views of Biden’s job approval and handling of other issues , a majority of Republicans (72%) with some awareness of the Willow project say they favor the decision. 

A majority of Democrats familiar with the project oppose the Biden administration’s decision (61%).

These patterns are broadly in keeping with partisan views on energy priorities for the country and support for more offshore oil and gas drilling.

Differences among Republicans by ideology, age over views about fossil fuels and prioritizing renewable energy sources

Views among Republicans about these issues are far from uniform. Conservative Republicans are far more likely than moderate or liberal Republicans to support expanding fossil fuel energy sources.

For example, 71% of conservative Republicans favor prioritizing the production of fossil fuels over the development of alternative energy sources. Moderate and liberal Republicans tilt the other direction, with 36% saying they would prioritize fossil fuels and a majority (63%) saying they would prioritize development of alternative energy sources.

Similarly, a 73% majority of conservative Republicans favor more coal mining while 54% of moderate and liberal Republicans oppose this.

Age differences among Republicans are also common in views about energy issues, with older Republicans more likely than younger Republicans to support the continued use of fossil fuels. These differences hold even when controlling for ideology.

For example, 31% of Republicans ages 18 to 29 say the country should never stop using fossil fuels. In comparison, a majority of older Republicans (including 68% of Republicans ages 65 and older) say this.

Republicans under 30 are far less convinced that unexpected problems are ahead in a future energy transition that greatly reduces energy production from fossil fuel sources and increases production from renewable sources: 32% say unexpected problems are very likely, compared with 69% of Republicans ages 65 and older.

Past Center surveys have also found large differences between older and younger Republicans in views on energy issues and climate policy.

Majority of Americans continue to oppose phasing out gasoline cars and trucks by 2035

A majority of Americans remain opposed to phasing out gasoline cars and trucks by 2035. In the new survey, 40% of Americans favor this idea while 59% oppose it. The share of Americans who support phasing out gasoline cars and trucks is down 7 percentage points since 2021. Over this period of time, support for phasing out gasoline cars and trucks has ticked down among both Democrats and Republicans. Refer to the Appendix for this data.

When asked about their general reaction to the idea of phasing out the production of gas-powered vehicles, just 21% of Americans say they would be excited by the prospect. More than twice as many (45%) say they would be upset; 33% say their feelings would be neutral.

The U.S. transportation sector is the largest contributor to carbon emissions, and a majority of those emissions come from gasoline-powered passenger cars and trucks. In April, the Biden administration proposed new emission limits for automakers that would dramatically reduce the number of gasoline-powered cars and trucks automakers could sell. Several states are planning to ban the sale of gasoline cars and trucks after 2035.

Republicans oppose phasing out gasoline cars and trucks by 2035 by a large margin (84% to 16%). But views among Democrats are largely supportive: 64% favor phasing out gasoline cars and trucks, while 35% oppose this idea.

Liberal Democrats are especially supportive of phasing out gasoline cars and trucks (76% favor), while moderate and conservative Democrats are more closely divided (53% favor, 46% oppose).

Across both parties, younger Americans are more supportive of this shift. Among all adults under age 30, a 56% majority favor phasing out gasoline cars and trucks by 2035, while 43% oppose this. By contrast, majorities of adults ages 30 and older oppose phasing out gasoline cars and trucks. For instance, among those ages 65 and older, 69% oppose this idea.

There are also wide differences in emotional reactions to the idea of phasing out the production of combustion-engine vehicles.

About three-quarters of Republicans (73%) say they would feel upset if gas cars were phased out. Among Democrats, a larger share say they would feel excited (37%) than upset (20%), while 43% of this group says they would feel neutral about the change.

While the Biden administration has attempted to develop electric vehicle (EV) infrastructure, Americans have limited confidence that the country will build a network of charging stations to support these vehicles.

Just 17% of Americans say they are extremely or very confident that the U.S. will build the necessary charging stations and infrastructure while 30% are somewhat confident; 53% say they are not too or not at all confident.

Legislation passed during the Biden administration set aside $5 billion to build a network of charging stations. Many EV drivers find the current public charging system difficult . Republicans are particularly skeptical that the U.S. will build the charging stations and infrastructure needed to support EVs: 74% have not too much or no confidence this will happen.

38% of Americans would consider an electric vehicle for their next purchase; half say they would be unlikely to do this

Americans are cool to the idea of making an electric vehicle purchase in the near term. Half of U.S. adults say they are not too or not at all likely to seriously consider an EV the next time they purchase a car or truck.

By comparison, 38% of Americans say they are very (15%) or somewhat (23%) likely to seriously consider an EV for their next vehicle purchase.

The public’s modest enthusiasm for purchasing an EV themselves is in line with their opposition to phasing out gas-powered vehicles. Interest in purchasing an EV is down 4 percentage points from when it was last measured in a 2022 Center survey .

Those most inclined to consider purchasing an electric vehicle in the future include the relatively small share of Americans (9%) who already own a hybrid or electric vehicle; 68% of this group says they are at least somewhat likely to seriously consider this, including four-in-ten who say they are very likely to do so.

Other groups who are more open to purchasing an EV in the future include Democrats (56% say they are at least somewhat likely to give this serious consideration), people who live in urban areas (48%), and young adults ages 18 to 29 (48%).

On the other hand, a majority of Republicans (70%), those who live in rural areas (65%) and adults ages 65 and older (59%) say they are not too or not at all likely to seriously consider an electric vehicle for their next purchase.

Americans who are at least somewhat likely to purchase an EV in the future say that both environmental benefits and cost savings are an attraction. About seven-in-ten of this group say that helping the environment (72%) and saving money on gas (70%) are major reasons to purchase an electric vehicle.

A much smaller share of those at least somewhat likely to consider purchasing an EV say that “keeping up with the latest trends in vehicles” is a consideration.

U.S. homeowners have a limited appetite for converting home systems to electric

There’s modest interest among homeowners in steps that would improve the energy efficiency of their homes by doing things like converting gas stoves and water heaters to electric. Some steps garner more appeal than others, however, and many have considered upgrading (or have already upgraded) their home’s insulation.

Three-in-ten homeowners say they have improved their home’s insulation in the past and another 26% say they have seriously considered doing so in the past year. A third of homeowners say they have not seriously considered a home insulation upgrade and 10% say this does not apply to their situation.

About three-in-ten homeowners (28%) say they have seriously considered installing solar panels on their home in the past year; 7% of homeowners say they already have home solar panels.

Homeowners living in the West are more likely to say they have solar panels. Democratic homeowners across all regions are more likely than their Republican counterparts to say they have seriously considered installing solar panels within the past year. These patterns are in keeping with a 2022 Center survey . (Note that the response options in the new survey include an option to specify that installing solar panels “does not apply to me,” thus the percentages are not directly comparable across the two surveys.)

Relatively small shares of homeowners say they have seriously considered replacing their gas oven or stove with an electric or induction system (7%), installing an electric heat pump system to heat and cool their home (11%), or replacing their gas water heater with an electric system (11%).

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Table of contents, how americans view electric vehicles, fast facts about international views of climate change as biden attends un cop26 conference, 67% of americans perceive a rise in extreme weather, but partisans differ over government efforts to address it, most u.s. latinos say global climate change and other environmental issues impact their local communities, on climate change, republicans are open to some policy approaches, even as they assign the issue low priority, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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Press Release

U.S. Department of Energy Announces $10 Million to Explore Using Plants to Extract Critical Materials from Soil to Support Domestic Supply Chain

WASHINGTON, D.C. — The U.S. Department of Energy (DOE) today announced up to $10 million in funding to explore nickel extraction from soil using plants—a process known as phytomining—to establish a competitive domestic supply chain, supplement conventional mining methods, and reduce nickel imports. Managed by the Advanced Research Projects Agency-Energy (ARPA-E), this effort supports  President Biden’s Investing in America agenda to strengthen domestic critical materials supply chains, enhance our economic and national security, and meet the growing demand for critical materials needed to ensure America leads the world in the emerging clean energy economy.

“In order to accomplish the goals laid out by President Biden to meet our clean energy targets, and support our economy and national security, it’s going to take all-hands-on-deck approach and innovative solutions,”  said ARPA-E Director Evelyn N. Wang.  “By exploring phytomining to extract nickel as the first target critical material, ARPA-E aims to achieve a cost-competitive and low-carbon footprint extraction approach needed to support the energy transition.”

Among the critical materials named in the DOE  Critical Materials Assessment (CMA), nickel serves as an ideal target to validate the viability of phytomining in the U.S. due to the large number of documented nickel hyperaccumulation (HA) plants. Nickel is used in the cathodes of lithium-ion batteries present in electric vehicles, consumer electronics, stationary storage, stainless steel, metallurgy, coatings, electroplating, and other alloys. Nickel is crucial to global clean energy technology supply chains and future demand is expected to grow.

The new ARPA-E Exploratory Topic announced today, Plant HYperaccumulators TO MIne Nickel-Enriched Soils (PHYTOMINES) , seeks to spur the technological development of phytomining in the United States that could complement current and future domestic sources of nickel and catalyze phytomining of critical minerals beyond nickel.

The targeted outcomes of PHYTOMINES are: 

• Technologies could be interventions in the soil or plant microbiome or the development of plant traits that enable the accumulation of nickel at an enhanced rate. ARPA-E envisions these projects as early-stage proof-of-concepts likely to take place in closed or open-air laboratories, greenhouses, or confined fields where light, humidity, and temperature regimes can be fully programmed.
• Possible projects include mapping HA species of interest, mineral characteristics in soil, and land ownership data for natural habitats and adjacent areas viable for phytomining, scaling opportunities, and technoeconomic and lifecycle analyses of phytomining projects.

PHYTOMINES encourages partnerships between farmers, scientists, battery manufacturers, steel and mining industries, and more. You can access more information on ARPA-E Exchange .

ARPA-E advances high-potential, high-impact clean energy technologies across a wide range of technical areas that are strategic to America's energy security. Learn more about these efforts and ARPA-E's commitment to ensuring the United States continues to lead the world in developing and deploying advanced clean energy technologies. 

Press and General Inquiries: 202-287-5440 [email protected]

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Dark energy survey looks 11 billion years into the past, reveals most detailed view ever of expanding universe

Researchers have measured the expansion history of the universe with the highest precision ever, providing a more detailed look at the nature of dark energy and its effect on the universe.

The results are from an analysis of spectra of galaxies and quasars recorded by the Dark Energy Spectroscopic Instrument (DESI). The data is from the first year of a five-year survey which will be used to create the largest 3D map of the universe ever made.

"This project is addressing some of the biggest questions in astronomy, like the nature of the mysterious dark energy that drives the expansion of the Universe," says Chris Davis, program director for NSF NOIRLab.

DESI is an international collaboration of more than 900 researchers from over 70 institutions around the world. The instrument is mounted on the NSF Nicholas U. Mayall 4-meter Telescope at Kitt Peak National Observatory. The DESI project is managed by the U.S. Department of Energy’s Lawrence Berkeley National Laboratory and funded by the DOE Office of Science.

This article was originally published by NSF NOIRLab. It has been edited for length and style.

• Read the full story: DESI Looks 11 Billion Years Into the Past to Reveal Most Detailed View Ever of the Expanding Universe

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