Machine Learning Method Could Speed the Search for New Battery Materials

To discover materials for better batteries, researchers must wade through a vast field of candidates. New research demonstrates a machine learning technique that could more quickly surface ones with the most desirable properties.

The study could accelerate designs for solid-state batteries, a promising next-generation technology that has the potential to store more energy than lithium-ion batteries without the flammability concerns. However, solid-state batteries encounter problems when materials within the cell interact with each other in ways that degrade performance.

Researchers from the National Renewable Energy Laboratory (NREL), the Colorado School of Mines, and the University of Illinois demonstrated a machine learning method that can accurately predict the properties of inorganic compounds. The work is led by NREL and part of DIFFERENTIATE, an initiative funded by the U.S. Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) that aims to speed energy innovation by incorporating artificial intelligence.

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