Machine learning a general purpose interatomic potential for silicon

TL;DR

This paper introduces a Gaussian Approximation Potential for silicon that accurately models various phases and defects, enabling complex simulations beyond traditional first-principles methods with uncertainty quantification.

Contribution

The paper presents a novel machine learning interatomic potential for silicon that reproduces DFT results across multiple phases and defects, demonstrating broad applicability and improved efficiency.

Findings

Accurately reproduces DFT results for silicon's phases and defects.

Enables computationally expensive simulations like phase boundaries and fracture.

Provides uncertainty estimates for atomic configurations.

Abstract

The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations on simulated length and time scales due to computational cost and its scaling. Techniques based on machine learning ideas for interpolating the Born-Oppenheimer potential energy surface without explicitly describing electrons have recently shown great promise, but accurately and efficiently fitting the physically relevant space of configurations has remained a challenging goal. Here we present a Gaussian Approximation Potential for silicon that achieves this milestone, accurately reproducing density functional theory reference results for a wide range of observable properties, including crystal, liquid, and amorphous bulk phases, as well as point, line, and plane defects. We demonstrate that this new potential enables calculations that would be extremely expensive with a first principles electronic structure method, such as finite temperature phase boundary lines, self-diffusivity in the liquid, formation of the amorphous by slow quench, and dynamic brittle fracture. We show that the uncertainty quantification inherent to the Gaussian process regression framework gives a qualitative estimate of the potential's accuracy for a given atomic configuration. The success of this model shows that it is indeed possible to create a useful machine-learning-based interatomic potential that comprehensively describes a material, and serves as a template for the development of such models in the future.