Predicting materials properties without crystal structure: deep representation learning from stoichiometry
Nature Publishing Group UK
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Goodall, R. E. A., & Lee, A. A. (2020). Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nature Communications, 11 (1)https://doi.org/10.1038/s41467-020-19964-7
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.
Article, /639/638/563/983, /639/301, /639/301/1034/1037, article
External DOI: https://doi.org/10.1038/s41467-020-19964-7
This record's URL: https://www.repository.cam.ac.uk/handle/1810/314901