A Theoretical Case Study of the Generalisation of Machine-learned Potentials

TL;DR

This paper provides a theoretical and numerical analysis of how machine-learned interatomic potentials generalize to complex dislocation simulations, highlighting key factors like training data size and fitting virials that influence accuracy.

Contribution

It offers a rigorous theoretical framework explaining MLIP generalization, emphasizing the importance of fitting virials and guiding data set and loss function design.

Findings

Fitting virials is crucial for MLIP consistency in dislocation simulations.

Training data size and observation choices directly impact simulation accuracy.

Numerical experiments support best practices and offer new insights.

Abstract

Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challenge for their generalization to a broader range of systems outside the training set. Nevertheless, MLIPs have demonstrated impressive accuracy in predicting forces and energies in simulations involving intricate and complex structures. In this paper we aim to take steps towards rigorously explaining the excellent observed generalisation properties of MLIPs. Specifically, we offer a comprehensive theoretical and numerical investigation of the generalization of MLIPs in the context of dislocation simulations. We quantify precisely how the accuracy of such simulations is directly determined by a few key factors: the size of the training structures, the choice of training observations (e.g., energies, forces, virials), and the level of accuracy achieved in the fitting process. Notably, our study reveals the crucial role of fitting virials in ensuring the consistency of MLIPs for dislocation simulations. Our series of careful numerical experiments encompassing screw, edge, and mixed dislocations, supports existing best practices in the MLIPs literature but also provides new insights into the design of data sets and loss functions.