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    Physically Informed Machine Learning Prediction of Electronic Density of States
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    Chemistry of Materials

    Cite this: Chem. Mater. 2022, 34, 11, 4848–4855
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    https://doi.org/10.1021/acs.chemmater.1c04252
    Published May 24, 2022
    Copyright © 2022 American Chemical Society
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    Abstract

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    The electronic structure of a material, such as its density of states (DOS), provides key insights into its physical and functional properties and serves as a valuable source of high-quality features for many materials screening and discovery workflows. However, the computational cost of calculating the DOS, most commonly with density functional theory (DFT), becomes prohibitive for meeting high-fidelity or high-throughput requirements, necessitating a cheaper but sufficiently accurate surrogate. To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT. This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity. We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints. This functionality provides a means for ensuring that the predicted DOS is reliable enough to be used as an input to downstream materials screening workflows to predict more complex functional properties, which rely on accurate physical features.

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    • Model hyperparameters, predicted DOS curves, error histograms, and extrapolated training size curves (PDF)

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    Cited By

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    This article is cited by 42 publications.

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    Chemistry of Materials

    Cite this: Chem. Mater. 2022, 34, 11, 4848–4855
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.chemmater.1c04252
    Published May 24, 2022
    Copyright © 2022 American Chemical Society
    Request reuse permissions

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