Use machine learning to find energy materials Posted on : Dec 09 - 2017

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. View More