2024/10/11 by Valerio Briganti, Alessandro Lunghi, Briganti, Valerio +1
Chemistry · Materials Science · Physics and Astronomy · #Advanced NMR Techniques and Applications #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Quantum and electron transport phenomena
paper · pdf · doi:10.48550/arxiv.2410.08912
openalex publication_date 2024/10/11 · openalex created_date 2024/10/16 · openalex updated_date 2026/07/28
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.