Random Spin Committee Approach For Smooth Interatomic Potentials

TL;DR

This paper introduces a novel random spin committee method to efficiently find ground states in spin-polarized systems, aiding the development of machine-learning interatomic potentials for complex materials.

Contribution

A new random spin committee approach is proposed for determining ground states in spin-polarized systems, improving the training data for machine-learning potentials.

Findings

Successfully applied to sulfur models with known ground states.

Achieved increasingly accurate fits for spin configurations.

Demonstrated potential for general bulk spin-polarized systems.

Abstract

Training interatomic potentials for spin-polarized systems continues to be a difficult task for the molecular modeling community. In this note, a proof-of-concept, random initial spin committee approach is proposed for obtaining the ground state of spin-polarized systems with a controllable degree of accuracy. The approach is tested on two toy models of elemental sulfur where the exact optimal spin configuration can be known. Machine-learning potentials are trained on the resulting data, and increasingly accurate fits with respect to the ground state are achieved, marking a step towards machine-learning force fields for general bulk spin-polarized systems.