Interatomic Interaction Models for Magnetic Materials: Recent Advances

TL;DR

This paper reviews recent advances in interatomic interaction models for magnetic materials, highlighting the development of magnetic machine-learning potentials that combine efficiency and accuracy, and discusses future research directions.

Contribution

It provides a comprehensive summary of models for magnetic materials, including traditional and machine-learning approaches, and offers insights into future developments.

Findings

Emergence of magnetic MLIPs combining efficiency and accuracy.

Summary of various models and their applications in magnetic materials.

Discussion of future prospects in interatomic potential development.

Abstract

Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study of magnetic materials. Initially, magnetic empirical interatomic potentials or spin-polarized density functional theory (DFT) served as the primary models for describing interatomic interactions in atomistic simulations of magnetic systems. Furthermore, in recent years, a new class of interatomic potentials known as magnetic machine-learning interatomic potentials (magnetic MLIPs) has emerged. These MLIPs combine the computational efficiency, in terms of CPU time, of empirical potentials with the accuracy of DFT calculations. In this review, our focus lies on providing a comprehensive summary of the interatomic interaction models developed specifically for investigating magnetic materials. We also delve into the various problem classes to which these models can be applied. Finally, we offer insights into the future prospects of interatomic interaction model development for the exploration of magnetic materials.