Unsupervised discovery of solid-state lithium ion conductors

作者：Ying Zhang, Xingfeng He, Zhiqian Chen, Qiang Bai, Adelaide M. Nolan, Charles A. Roberts, Debasish Banerjee, Tomoya Matsunaga, Yifei Mo, Chen Ling · 发表于：Nature Communications · 年份：2019 · DOI：10.1038/s41467-019-13214-1 · 被引用次数：325 · 研究领域：Machine Learning in Materials Science、Fuel Cells and Related Materials、X-ray Diffraction in Crystallography

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.