A machine-learning framework for accelerating spin-lattice relaxation simulations

TL;DR

This paper introduces a machine-learning framework that significantly accelerates spin-lattice relaxation simulations by predicting molecular vibrations and spin-phonon couplings, reducing computational costs while maintaining accuracy.

Contribution

The authors develop a novel machine-learning based method that reduces ab initio computational costs by approximately 80% for spin relaxation simulations.

Findings

Achieves semi-to-full quantitative agreement with ab initio methods.

Reduces computational cost by about 80%.

Extends naturally to molecular dynamics simulations.

Abstract

Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence. Ab initio simulations have played a fundamental role in shaping our understanding of this process but further progress is hindered by their high computational cost. Here we present an accelerated computational framework based on machine-learning models for the prediction of molecular vibrations and spin-phonon coupling coefficients. We apply this method to three open-shell coordination compounds exhibiting long relaxation times and show that this approach achieves semi-to-full quantitative agreement with ab initio methods reducing the computational cost by about 80%. Moreover, we show that this framework naturally extends to molecular dynamics simulations, paving the way to the study of spin relaxation in condensed matter beyond simple equilibrium harmonic thermal baths.