Work

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

Year: 2025

Type: article

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.

Source: npj Computational Materials

DOI: 10.1038/s41524-025-01547-z

Authors: Valerio Briganti, Alessandro Lunghi

Institutions: Trinity College Dublin, Advanced Materials and BioEngineering Research

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,490

Funders: European Commission, Irish Centre for High-End Computing