Environment-adaptive machine learning potentials

TL;DR

This paper introduces environment-adaptive machine learning potentials that dynamically adjust to local atomic environments, enabling accurate modeling of diverse physical phenomena and conditions in materials.

Contribution

It presents a novel method to construct environment-adaptive interatomic potentials using clustering and a many-body expansion for smooth blending.

Findings

Accurately predicts properties of Ta and InP systems

Demonstrates smooth potential energy surface across environments

Aligns well with density functional theory results

Abstract

The development of interatomic potentials that can accurately capture a wide range of physical phenomena and diverse environments is of significant interest, but it presents a formidable challenge. This challenge arises from the numerous structural forms, multiple phases, complex intramolecular and intermolecular interactions, and varying external conditions. In this paper, we present a method to construct environment-adaptive interatomic potentials by adapting to the local atomic environment of each atom within a system. The collection of atomic environments of interest is partitioned into several clusters of atomic environments. Each cluster represents a distinctive local environment and is used to define a corresponding local potential. We introduce a many-body many-potential expansion to smoothly blend these local potentials to ensure global continuity of the potential energy surface. This is achieved by computing the probability functions that determine the likelihood of an atom belonging to each cluster. We apply the environment-adaptive machine learning potentials to predict observable properties for Ta element and InP compound, and compare them with density functional theory calculations.