Pushing the limits of unconstrained machine-learned interatomic potentials

TL;DR

This paper explores the potential of unconstrained machine-learned interatomic potentials (MLIPs), demonstrating that with large datasets, they can outperform constrained models in accuracy and efficiency, while maintaining physical consistency.

Contribution

The study shows that unconstrained MLIPs, when trained on large datasets, can surpass constrained models in accuracy and speed, with simple modifications ensuring physical symmetry compliance.

Findings

Unconstrained MLIPs can be more accurate than constrained ones when trained on large datasets.

Unconstrained models offer faster inference in practical atomic simulation workflows.

Simple inference-time modifications can restore physical symmetries in unconstrained MLIPs.

Abstract

Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The most commonly used model architectures are constrained to fulfill a number of physical laws exactly, from geometric symmetries to energy conservation. Evidence is mounting that relaxing some of these constraints can be beneficial to the efficiency and (somewhat surprisingly) accuracy of MLIPs, even though care should be taken to avoid qualitative failures associated with the breaking of physical symmetries. Given the recent trend of scaling up models to larger numbers of parameters and training samples, a very important question is how unconstrained MLIPs behave in this limit. Here we investigate this issue, showing that -- when trained on large datasets -- unconstrained models can be superior in accuracy and speed when compared to physically constrained models. We assess these models both in terms of benchmark accuracy and in terms of usability in practical scenarios, focusing on static simulation workflows such as geometry optimization and lattice dynamics. We conclude that accurate unconstrained models can be applied with confidence, especially since simple inference-time modifications can be used to recover observables that are consistent with the relevant physical symmetries.