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Predicting materials properties without crystal structure: deep representation learning from stoichiometry

Published version
Peer-reviewed

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Abstract

Abstract: Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use descriptors constructed from knowledge of either the full crystal structure — therefore only applicable to materials with already characterised structures — or structure-agnostic fixed-length representations hand-engineered from the stoichiometry. We develop a machine learning approach that takes only the stoichiometry as input and automatically learns appropriate and systematically improvable descriptors from data. Our key insight is to treat the stoichiometric formula as a dense weighted graph between elements. Compared to the state of the art for structure-agnostic methods, our approach achieves lower errors with less data.

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Keywords

Article, /639/638/563/983, /639/301, /639/301/1034/1037, article

Journal Title

Nature Communications

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Journal ISSN

2041-1723

Volume Title

11

Publisher

Nature Publishing Group UK