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Towards exact molecular dynamics simulations with machine-learned force fields

Author

Listed:
  • Stefan Chmiela

    (Technische Universität Berlin)

  • Huziel E. Sauceda

    (Fritz-Haber-Institut der Max-Planck-Gesellschaft)

  • Klaus-Robert Müller

    (Technische Universität Berlin
    Korea University
    Max Planck Institute for Informatics)

  • Alexandre Tkatchenko

    (University of Luxembourg)

Abstract

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.

Suggested Citation

  • Stefan Chmiela & Huziel E. Sauceda & Klaus-Robert Müller & Alexandre Tkatchenko, 2018. "Towards exact molecular dynamics simulations with machine-learned force fields," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06169-2
    DOI: 10.1038/s41467-018-06169-2
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    Citations

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    Cited by:

    1. J. Thorben Frank & Oliver T. Unke & Klaus-Robert Müller & Stefan Chmiela, 2024. "A Euclidean transformer for fast and stable machine learned force fields," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. 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.
    3. 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.
    4. Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. Yusong Wang & Tong Wang & Shaoning Li & Xinheng He & Mingyu Li & Zun Wang & Nanning Zheng & Bin Shao & Tie-Yan Liu, 2024. "Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Yuanming Bai & Leslie Vogt-Maranto & Mark E. Tuckerman & William J. Glover, 2022. "Machine learning the Hohenberg-Kohn map for molecular excited states," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. Albert Musaelian & Simon Batzner & Anders Johansson & Lixin Sun & Cameron J. Owen & Mordechai Kornbluth & Boris Kozinsky, 2023. "Learning local equivariant representations for large-scale atomistic dynamics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    8. Simon Batzner & Albert Musaelian & Lixin Sun & Mario Geiger & Jonathan P. Mailoa & Mordechai Kornbluth & Nicola Molinari & Tess E. Smidt & Boris Kozinsky, 2022. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    9. Hanwen Zhang & Veronika Juraskova & Fernanda Duarte, 2024. "Modelling chemical processes in explicit solvents with machine learning potentials," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    10. David Buterez & Jon Paul Janet & Steven J. Kiddle & Dino Oglic & Pietro Lió, 2024. "Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    11. Eric C.-Y. Yuan & Anup Kumar & Xingyi Guan & Eric D. Hermes & Andrew S. Rosen & Judit Zádor & Teresa Head-Gordon & Samuel M. Blau, 2024. "Analytical ab initio hessian from a deep learning potential for transition state optimization," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    12. Charlotte Loh & Thomas Christensen & Rumen Dangovski & Samuel Kim & Marin Soljačić, 2022. "Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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