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Analytical ab initio hessian from a deep learning potential for transition state optimization

Author

Listed:
  • Eric C.-Y. Yuan

    (University of California
    Lawrence Berkeley National Laboratory)

  • Anup Kumar

    (Lawrence Berkeley National Laboratory)

  • Xingyi Guan

    (University of California
    Lawrence Berkeley National Laboratory)

  • Eric D. Hermes

    (Quantum-Si)

  • Andrew S. Rosen

    (Lawrence Berkeley National Laboratory
    University of California)

  • Judit Zádor

    (Sandia National Laboratories)

  • Teresa Head-Gordon

    (University of California
    Lawrence Berkeley National Laboratory
    University of California)

  • Samuel M. Blau

    (Lawrence Berkeley National Laboratory)

Abstract

Identifying transition states—saddle points on the potential energy surface connecting reactant and product minima—is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2–3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52481-5
    DOI: 10.1038/s41467-024-52481-5
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    References listed on IDEAS

    as
    1. Seonghwan Kim & Jeheon Woo & Woo Youn Kim, 2024. "Diffusion-based generative AI for exploring transition states from 2D molecular graphs," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. 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.
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