Reversible molecular simulation for training classical and machine learning force fields

Joe G Greener

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arXiv · 2412.04374

Reversible molecular simulation for training classical and machine learning force fields

Joe G Greener

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Abstract

The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine learning potentials. Differentiable molecular simulation calculates gradients of observables with respect to parameters through molecular dynamics trajectories. Here we improve this approach by explicitly calculating gradients using a reverse-time simulation with effectively constant memory cost and a computation count similar to the forward simulation. The method is applied to learn all-atom water and gas diffusion models with different functional forms, and to train a machine learning potential for diamond from scratch. Comparison to ensemble reweighting indicates that reversible simulation can provide more accurate gradients and train to match time-dependent observables.

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Joe G Greener. 2024-12-05. Reversible molecular simulation for training classical and machine learning force fields. https://arxiv.org/abs/2412.04374

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