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Interpolating numerically exact many-body wave functions for accelerated molecular dynamics

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  • Yannic Rath

    (National Physical Laboratory
    King’s College London)

  • George H. Booth

    (King’s College London)

Abstract

While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We develop an approach to bridge these fields with a practical interpolation scheme for the correlated many-electron state through the space of atomic configurations, whilst avoiding the exponential complexity of these underlying electronic states. With a small number of accurate correlated wave functions as a training set, we demonstrate provable convergence to near-exact potential energy surfaces for subsequent dynamics with propagation of a valid many-body wave function and inference of its variational energy whilst retaining a mean-field computational scaling. This represents a profoundly different paradigm to the direct interpolation of potential energy surfaces in established machine-learning approaches. We combine this with modern electronic structure approaches to systematically resolve molecular dynamics trajectories and converge thermodynamic quantities with a high-throughput of several million interpolated wave functions with explicit validation of their accuracy from only a few numerically exact quantum chemical calculations. We also highlight the comparison to traditional machine-learned potentials or dynamics on mean-field surfaces.

Suggested Citation

  • Yannic Rath & George H. Booth, 2025. "Interpolating numerically exact many-body wave functions for accelerated molecular dynamics," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57134-9
    DOI: 10.1038/s41467-025-57134-9
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    References listed on IDEAS

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    1. Yangjun Wu & Xiansong Xu & Dario Poletti & Yi Fan & Chu Guo & Honghui Shang, 2023. "A Real Neural Network State for Quantum Chemistry," Mathematics, MDPI, vol. 11(6), pages 1-10, March.
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