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Quantum chemical accuracy from density functional approximations via machine learning

Author

Listed:
  • Mihail Bogojeski

    (Machine Learning Group, Technische Universität Berlin)

  • Leslie Vogt-Maranto

    (New York University)

  • Mark E. Tuckerman

    (New York University
    Courant Institute of Mathematical Science, New York University
    NYU-ECNU Center for Computational Chemistry at NYU Shanghai)

  • Klaus-Robert Müller

    (Machine Learning Group, Technische Universität Berlin
    Korea University
    Max-Planck-Institut für Informatik)

  • Kieron Burke

    (University of California
    University of California)

Abstract

Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol−1 with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol−1) on test data. Moreover, density-based Δ-learning (learning only the correction to a standard DFT calculation, termed Δ-DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Δ-DFT is highlighted by correcting “on the fly” DFT-based molecular dynamics (MD) simulations of resorcinol (C6H4(OH)2) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Δ-DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails.

Suggested Citation

  • Mihail Bogojeski & Leslie Vogt-Maranto & Mark E. Tuckerman & Klaus-Robert Müller & Kieron Burke, 2020. "Quantum chemical accuracy from density functional approximations via machine learning," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19093-1
    DOI: 10.1038/s41467-020-19093-1
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    Cited by:

    1. 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.
    2. Yubo Chen & Joon Kyo Seo & Yuanmiao Sun & Thomas A. Wynn & Marco Olguin & Minghao Zhang & Jingxian Wang & Shibo Xi & Yonghua Du & Kaidi Yuan & Wei Chen & Adrian C. Fisher & Maoyu Wang & Zhenxing Feng , 2022. "Enhanced oxygen evolution over dual corner-shared cobalt tetrahedra," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

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