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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Author

Listed:
  • Justin S. Smith

    (University of Florida
    Los Alamos National Laboratory
    Los Alamos National Laboratory)

  • Benjamin T. Nebgen

    (Los Alamos National Laboratory
    Los Alamos National Laboratory)

  • Roman Zubatyuk

    (Los Alamos National Laboratory
    Jackson State University)

  • Nicholas Lubbers

    (Los Alamos National Laboratory
    Los Alamos National Laboratory)

  • Christian Devereux

    (University of Florida)

  • Kipton Barros

    (Los Alamos National Laboratory)

  • Sergei Tretiak

    (Los Alamos National Laboratory
    Los Alamos National Laboratory)

  • Olexandr Isayev

    (University of North Carolina at Chapel Hill)

  • Adrian E. Roitberg

    (University of Florida)

Abstract

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.

Suggested Citation

  • Justin S. Smith & Benjamin T. Nebgen & Roman Zubatyuk & Nicholas Lubbers & Christian Devereux & Kipton Barros & Sergei Tretiak & Olexandr Isayev & Adrian E. Roitberg, 2019. "Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10827-4
    DOI: 10.1038/s41467-019-10827-4
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    Cited by:

    1. Zeyin Yan & Dacong Wei & Xin Li & Lung Wa Chung, 2024. "Accelerating reliable multiscale quantum refinement of protein–drug systems enabled by machine learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Tian Xie & Arthur France-Lanord & Yanming Wang & Jeffrey Lopez & Michael A. Stolberg & Megan Hill & Graham Michael Leverick & Rafael Gomez-Bombarelli & Jeremiah A. Johnson & Yang Shao-Horn & Jeffrey C, 2022. "Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Peikun Zheng & Roman Zubatyuk & Wei Wu & Olexandr Isayev & Pavlo O. Dral, 2021. "Artificial intelligence-enhanced quantum chemical method with broad applicability," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    4. 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.
    5. Shuai Jiang & Yi-Rong Liu & Teng Huang & Ya-Juan Feng & Chun-Yu Wang & Zhong-Quan Wang & Bin-Jing Ge & Quan-Sheng Liu & Wei-Ran Guang & Wei Huang, 2022. "Towards fully ab initio simulation of atmospheric aerosol nucleation," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    6. Junjie Wang & Yong Wang & Haoting Zhang & Ziyang Yang & Zhixin Liang & Jiuyang Shi & Hui-Tian Wang & Dingyu Xing & Jian Sun, 2024. "E(n)-Equivariant cartesian tensor message passing interatomic potential," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. 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.
    8. 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.
    9. Pushkar G. Ghanekar & Siddharth Deshpande & Jeffrey Greeley, 2022. "Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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