Unsupervised discovery of solid-state lithium ion conductors

Ying Zhang, Xingfeng He, Zhiqian Chen, Qiang Bai, Adelaide M. Nolan, Charles A. Roberts, Debasish Banerjee, Tomoya Matsunaga, Yifei Mo () and Chen Ling ()
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Ying Zhang: Toyota Research Institute of North America
Xingfeng He: University of Maryland
Zhiqian Chen: Virginia Tech
Qiang Bai: University of Maryland
Adelaide M. Nolan: University of Maryland
Charles A. Roberts: Toyota Research Institute of North America
Debasish Banerjee: Toyota Research Institute of North America
Tomoya Matsunaga: Toyota Research Institute of North America
Yifei Mo: University of Maryland
Chen Ling: Toyota Research Institute of North America

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: Abstract Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.

Date: 2019
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DOI: 10.1038/s41467-019-13214-1

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