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Discovering promising new materials is central to our ability to design better batteries, but research progress over the last several decades has been limited by an incomplete understanding of the materials physics and inefficient guess-and-check searches. Our research seeks to overcome these limitations by leveraging new approaches inspired by machine learning to make predictions of material performance from existing experimental data. Focusing on solid-state electrolyte materials, we build a data-driven model for predicting ionic conductivity from experimental data on crystal structure and ionic conductivity from the literature. We use the resulting model to guide an experimental search for high ionic conductivity electrolyte materials, and find that incorporating machine learning yields a 3-5x increase in the discovery of high ionic conductivity materials over a comparable guess-and-check effort. This approach also enables us to quantify the likelihood of additional future breakthrough materials discoveries, and we find, among other insights, that one is nearly 100x more likely to realize a stable solid-state battery with two electrolytes rather than one.
Austin Sendek is a fifth-year Ph.D. student in the Department of Applied Physics at Stanford working with Professors Evan Reed and Yi Cui. Sendek’s research focuses on leveraging machine learning to accelerate the discovery and design process in materials for solid-state lithium ion batteries. More generally, he is interested in making the innovation process in energy and environmental technologies smarter, leaner, and faster. Sendek is a GCEP Distinguished Student Lecturer and his research has been featured on ScienceDirect, Scientific Computing magazine, and San Francisco’s ABC7 News. Sendek has served as the Vice President and Co-President of the Stanford Energy Club, Stanford’s largest student-run energy organization, and he was a member of the 2016 cohort of the Woods Institute’s Rising Environmental Leaders Program. In 2010, he started a whimsical movement to designate the Northern California slang term “hella” as the SI prefix for one octillion, a proposal that was discussed in The Economist, LA Times, and CNN. He holds a B.S. in Applied Physics with highest honors from the University of California, Davis.
Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., Reed, E. J., Holistic Computational Structure Screening of more than 12,000 Candidates for Solid Lithium-ion Conductor Materials. Energy & Environmental Science, doi:10.1039/C6EE02697D (2016).