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Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

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  • Stephan Thaler

    (TUM School of Engineering and Design, Technical University of Munich)

  • Julija Zavadlav

    (TUM School of Engineering and Design, Technical University of Munich
    Technical University of Munich)

Abstract

In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27241-4
    DOI: 10.1038/s41467-021-27241-4
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    References listed on IDEAS

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    1. Maciej Majewski & Adrià Pérez & Philipp Thölke & Stefan Doerr & Nicholas E. Charron & Toni Giorgino & Brooke E. Husic & Cecilia Clementi & Frank Noé & Gianni Fabritiis, 2023. "Machine learning coarse-grained potentials of protein thermodynamics," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Mokshin, Anatolii V. & Khabibullin, Roman A., 2022. "Is there a one-to-one correspondence between interparticle interactions and physical properties of liquid?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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