Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data

TL;DR

This paper explores the use of symbolic regression, particularly genetic programming, to discover interatomic potentials from data, offering a transparent and efficient alternative to traditional methods in materials science.

Contribution

It introduces a genetic programming approach for modeling atomic potentials from raw data and validates its effectiveness on ab initio electronic structure data.

Findings

Successfully modeled atomic potentials using symbolic regression

Validated approach on ab initio electronic structure data

Demonstrated potential for efficient, interpretable interatomic potentials

Abstract

Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow to calculate the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however their aplicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful "white-box" approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data.