Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics

TL;DR

This paper introduces a data-driven framework for creating magneto-elastic machine-learning interatomic potentials that enable accurate large-scale spin-lattice dynamics simulations, validated on {\

Contribution

It develops a novel coupling of atomic spin models with ML-IAPs to accurately predict magneto-elastic properties from first-principles data.

Findings

Accurately predicts properties of {\

Demonstrates effectiveness in modeling phase transitions and material properties.

Abstract

A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for {\alpha}-iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic-paramagnetic phase transition.