Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
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Lee, A., & Goodall, R. Predicting materials properties without crystal structure: Deep representation learning from stoichiometry. Nature Communications https://doi.org/10.17863/CAM.59792
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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This record's DOI: https://doi.org/10.17863/CAM.59792
This record's URL: https://www.repository.cam.ac.uk/handle/1810/312694
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