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Quantum-chemical insights from deep tensor neural networks

Author

Listed:
  • Kristof T. Schütt

    (Machine Learning Group, Technische Universität Berlin)

  • Farhad Arbabzadah

    (Machine Learning Group, Technische Universität Berlin)

  • Stefan Chmiela

    (Machine Learning Group, Technische Universität Berlin)

  • Klaus R. Müller

    (Machine Learning Group, Technische Universität Berlin
    Korea University, Anam-dong, Seongbuk-gu)

  • Alexandre Tkatchenko

    (Fritz-Haber-Institut der Max-Planck-Gesellschaft
    Physics and Materials Science Research Unit, University of Luxembourg, Luxembourg,)

Abstract

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

Suggested Citation

  • Kristof T. Schütt & Farhad Arbabzadah & Stefan Chmiela & Klaus R. Müller & Alexandre Tkatchenko, 2017. "Quantum-chemical insights from deep tensor neural networks," Nature Communications, Nature, vol. 8(1), pages 1-8, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms13890
    DOI: 10.1038/ncomms13890
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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. Xiao Tan & Yuan Zhou & Zuohua Ding & Yang Liu, 2021. "Selecting Correct Methods to Extract Fuzzy Rules from Artificial Neural Network," Mathematics, MDPI, vol. 9(11), pages 1-22, May.
    3. Xing Chen & Flavio Abreu Araujo & Mathieu Riou & Jacob Torrejon & Dafiné Ravelosona & Wang Kang & Weisheng Zhao & Julie Grollier & Damien Querlioz, 2022. "Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. 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.
    5. 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.
    6. 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.
    7. Nikita Moshkov & Tim Becker & Kevin Yang & Peter Horvath & Vlado Dancik & Bridget K. Wagner & Paul A. Clemons & Shantanu Singh & Anne E. Carpenter & Juan C. Caicedo, 2023. "Predicting compound activity from phenotypic profiles and chemical structures," Nature Communications, Nature, vol. 14(1), pages 1-11, 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. Alessio Fallani & Leonardo Medrano Sandonas & Alexandre Tkatchenko, 2024. "Inverse mapping of quantum properties to structures for chemical space of small organic molecules," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    10. Oliver T. Unke & Stefan Chmiela & Michael Gastegger & Kristof T. Schütt & Huziel E. Sauceda & Klaus-Robert Müller, 2021. "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    11. Stephan Thaler & Julija Zavadlav, 2021. "Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting," Nature Communications, Nature, vol. 12(1), pages 1-10, 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.
    13. Sunghwan Choi, 2023. "Prediction of transition state structures of gas-phase chemical reactions via machine learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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