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BIGDML—Towards accurate quantum machine learning force fields for materials

Author

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  • Huziel E. Sauceda

    (Universidad Nacional Autónoma de México
    Technische Universität Berlin
    Technische Universität Berlin)

  • Luis E. Gálvez-González

    (Universidad de Sonora, Blvd. Luis Encinas & Rosales)

  • Stefan Chmiela

    (Technische Universität Berlin
    BIFOLD – Berlin Institute for the Foundations of Learning and Data)

  • Lauro Oliver Paz-Borbón

    (Universidad Nacional Autónoma de México)

  • Klaus-Robert Müller

    (Technische Universität Berlin
    BIFOLD – Berlin Institute for the Foundations of Learning and Data
    Google Research, Brain team
    Korea University, Anam-dong)

  • Alexandre Tkatchenko

    (University of Luxembourg)

Abstract

Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10–200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene–graphene dynamics induced by nuclear quantum effects and their strong contributions to the hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures.

Suggested Citation

  • Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31093-x
    DOI: 10.1038/s41467-022-31093-x
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    References listed on IDEAS

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    Cited by:

    1. Adil Kabylda & Valentin Vassilev-Galindo & Stefan Chmiela & Igor Poltavsky & Alexandre Tkatchenko, 2023. "Efficient interatomic descriptors for accurate machine learning force fields of extended molecules," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Yaolong Zhang & Bin Jiang, 2023. "Universal machine learning for the response of atomistic systems to external fields," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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