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| Photo: Edward Sargent |
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| A solar module on display at an expo in Tokyo. Photo: Yuriko Nakao/Reuters |
The world needs more energy. Governments and companies are investing billions of dollars in technologies to harvest, convert and store power1. And as silicon solar cells approach the limit of their performance, researchers are looking to alternatives based on perovskites and quantum dots2. '
The batteries that store the energy must get cheaper, more efficient and longer-lasting3. And devices need to be manufactured from safe and abundant materials such as copper, nickel and carbon rather than from lead, platinum or gold. Life-cycle analyses of the materials need to show improved carbon footprints, as well as the ability to match the scale of the global energy challenge.
Enormous quantities of experimental data are being generated on the properties of such materials. The US National Institute of Standards and Technology, for example, hosts 65 databases, some with as many as 67,500 measurements. Also, since 2010, more than 1.7 million scientific papers have been published on batteries and solar cells alone.
Relating the structure of a material to its function needs accelerating. The search space is vast. Many materials are still found empirically: candidates are made and tested a few samples at a time. Searches are subject to human bias. Researchers often focus on a few combinations of the elements that they deem interesting.
Computational methods are being developed that automatically generate structures and assess their electronic features and other properties4. The Materials Project, for instance, is using supercomputers to predict the properties of all known materials5. It currently lists predicted properties for more than 700,000 materials. But the tremendous potential to translate such data into industrial and commercial applications is still a long way from being realized.
Machine learning — algorithms trained to find patterns in data sets — could greatly speed up the discovery of energy materials. It has already been used to predict the results of quantum simulations to identify potential molecules and materials for flow batteries, organic light-emitting diodes6, organic photovoltaic cells and carbon dioxide conversion catalysts7. The algorithms can predict results in a few minutes, compared with the hundreds of hours it takes to run the simulations8.
Challenges remain, however. There is no universal representation for encoding materials. Different applications require different properties, such as elemental composition, crystal structure and conductivity. Well-curated experimental data on materials are rare, and computational tests of hypotheses rely on assumptions and models that may be far from realistic under experimental conditions.
The machine-learning and energy-sciences communities should collaborate more. They must understand each other’s capabilities and needs. We offer the following recommendations, which came out of a workshop run by the Canadian Institute for Advanced Research in May in Boston, Massachusetts...
WHAT NEXT
... more investment is needed in artificial intelligence and robotics-driven materials research throughout the world. More data must be made available to people programming the robots. And experimentalists, robotics experts and algorithm designers should communicate and collaborate more to facilitate rapid troubleshooting.
Time is running out to find the new energy technologies the world needs.
Read more...
Supplementary Information
Supplementary information d41586-017-07820-6
Source: Nature.com
