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Reversible molecular simulation for training classical and machine-learning force fields.

Joe G Greener¹

¹Medical Research Council Laboratory of Molecular Biology, Cambridge CB2 0QH, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America

|May 28, 2025

Summary

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Next-generation molecular dynamics force fields will use more data. We developed a faster, memory-efficient differentiable simulation method to accurately train machine-learning potentials using experimental data.

Area of Science:

Computational chemistry
Materials science
Machine learning

Background:

Developing accurate molecular dynamics force fields is crucial for simulating material properties.
Training machine-learning potentials with experimental data is challenging due to computational costs.
Differentiable molecular simulation offers a path to optimize force field parameters by calculating gradients.

Purpose of the Study:

To improve differentiable molecular simulation for efficient training of machine-learning potentials.
To develop a reverse-time simulation method with reduced memory and computational requirements.
To demonstrate the method's effectiveness in learning water, gas diffusion, and diamond potentials.

Main Methods:

Implemented a reverse-time simulation to explicitly calculate gradients of observables with respect to force field parameters.

Keywords:

differentiable force field molecular dynamics reversible

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Achieved effectively constant memory cost and computation count comparable to forward simulations.

Applied the method to learn all-atom water and gas diffusion models and a diamond machine-learning potential from scratch.

Main Results:

The reverse-time simulation method significantly enhances the efficiency of gradient calculation in differentiable molecular simulation.
The approach successfully learned accurate models for water and gas diffusion, and a diamond potential.
Comparison with ensemble reweighting showed that reversible simulation yields more accurate gradients.

Conclusions:

The improved differentiable simulation method provides a more efficient and accurate way to train machine-learning potentials.
This advancement facilitates the development of next-generation force fields leveraging large experimental datasets.
The technique is broadly applicable for optimizing molecular models in computational chemistry and materials science.