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

By Rhys E. A. Goodall, Alpha Albert Lee. Nature Communications, 2019

346 citations recorded by the source corpus.

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. Predicting the structure of unknown materials’ compositions represents a challenge for high-throughput computational approaches. Here the authors introduce a new stoichiometry-based machine learning approach for predicting the properties of inorganic materials from their elemental compositions.

Subfields: Computational Theory and Mathematics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/b91332c67e19b0f0e6c9546dc47bda75c9082488

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In Computational Theory and Mathematics

In Materials Chemistry

Citations in the corpus

Scaling deep learning for materials discovery (2023, methodology): “Following Roost (representation learning from stoichiometry) 58 , we find GNNs to be effective at predicting the formation energy of a composition and structure.”
Recent advances and applications of deep learning methods in materials science (2021, methodology): “Further, graph-based methods such as Roost 154 have also been developed which can outperform many similar techniques.”