Indirect Learning of Interatomic Potentials for Accelerated Materials Simulations

TL;DR

This paper introduces a method where a high-accuracy, computationally expensive ML potential trains a faster, less flexible potential, enabling efficient large-scale materials simulations without quantum calculations.

Contribution

The authors propose an indirect learning approach for interatomic potentials, reducing computational costs while maintaining accuracy in large-scale materials modeling.

Findings

Improved quality of fast potentials using indirect training.

Successful simulation of silicon vitrification and grain formation.

Scalability to systems with a million atoms.

Abstract

Machine learning (ML) based interatomic potentials are emerging tools for materials simulations but require a trade-off between accuracy and speed. Here we show how one can use one ML potential model to train another: we use an existing, accurate, but more computationally expensive model to generate reference data (locations and labels) for a series of much faster potentials. Without the need for quantum-mechanical reference computations at the secondary stage, extensive reference datasets can be easily generated, and we find that this improves the quality of fast potentials with less flexible functional forms. We apply the technique to disordered silicon, including a simulation of vitrification and polycrystalline grain formation under pressure with a system size of a million atoms. Our work provides conceptual insight into the machine learning of interatomic potential models, and it suggests a route toward accelerated simulations of condensed-phase systems and nanostructured materials.