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BIGDML-Towards accurate quantum machine learning force fields for materials.

Huziel E Sauceda1,2,3, Luis E Gálvez-González4, Stefan Chmiela5,6

  • 1Departamento de Materia Condensada, Instituto de Física, Universidad Nacional Autónoma de México, Cd. de México C.P., 04510, Mexico. huziel.sauceda@fisica.unam.mx.

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|June 29, 2022
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Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) creates accurate machine-learning force fields with minimal data. This approach enhances efficiency and applicability across diverse materials and interfaces.

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Area of Science:

  • Computational materials science
  • Machine learning in chemistry
  • Condensed matter physics

Background:

  • Machine-learning force fields (MLFFs) are crucial for simulating molecular and material systems.
  • Current MLFFs often require extensive training data and face limitations in chemical space applicability.
  • Tradeoffs between accuracy, data efficiency, and applicability hinder widespread use of MLFFs.

Purpose of the Study:

  • Introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach.
  • Demonstrate BIGDML's ability to construct reliable force fields with high data efficiency.
  • Validate BIGDML's performance across diverse material systems and interfaces.

Main Methods:

  • Developed the BIGDML model incorporating full material symmetry groups.
  • Trained BIGDML models using small datasets (10-200 geometries).
  • Performed extensive path-integral molecular dynamics simulations.

Main Results:

  • Achieved state-of-the-art energy accuracies (errors < 1 meV/atom) for semiconductors, metals, and adsorbates.
  • Demonstrated high data efficiency, requiring significantly less training data than conventional MLFFs.
  • Observed counterintuitive localization of benzene-graphene dynamics due to nuclear quantum effects.
  • Quantified strong contributions of nuclear quantum effects to hydrogen diffusion in Palladium.

Conclusions:

  • BIGDML offers a significant advancement in developing accurate and data-efficient machine-learning force fields.
  • The BIGDML approach overcomes limitations of existing MLFFs, expanding their applicability.
  • Nuclear quantum effects play a crucial role in material dynamics, as highlighted by BIGDML simulations.