Learning Interatomic Potentials at Multiple Scales

TL;DR

This paper presents a novel multi-scale learning approach for interatomic potentials that enables faster molecular dynamics simulations by co-training two neural network models to separate short- and long-range interactions.

Contribution

It introduces a method to learn scale separation in interatomic interactions by co-training two neural network potentials, enabling multi-time-step integration for faster MD simulations.

Findings

Achieved approximately 3x speedup in MD simulations.

Maintained accuracy in potential energy and simulation quantities.

Demonstrated effectiveness on complex interatomic interactions.

Abstract

The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a multiple-time-step (MTS) integrator that evaluates certain potential energy terms that vary more slowly than others less frequently. This approach is enabled by the simple but limiting analytic forms of classical potentials. Machine learning interatomic potentials (MLIPs), in particular recent equivariant neural networks, are much more broadly applicable than classical potentials and can faithfully reproduce the expensive but accurate reference electronic structure calculations used to train them. They still, however, require the use of a single short time step, as they lack the inherent term-by-term scale separation of classical potentials. This work introduces a method to learn a scale separation in complex interatomic interactions by co-training two MLIPs. Initially, a small and efficient model is trained to reproduce short-time-scale interactions. Subsequently, a large and expressive model is trained jointly to capture the remaining interactions not captured by the small model. When running MD, the MTS integrator then evaluates the smaller model for every time step and the larger model less frequently, accelerating simulation. Compared to a conventionally trained MLIP, our approach can achieve a significant speedup (~3x in our experiments) without a loss of accuracy on the potential energy or simulation-derived quantities.