Articlenpj computational materials2025
A machine-learning framework for accelerating spin-lattice relaxation simulations.
Article in npj computational materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Raman relaxation in Yb(iii) molecular qubits: non-trivial correlations between spin-phonon coupling and molecular structure.Chemical science · 2026Article
- From electron spin to relaxivity: a multidisciplinary perspective on first-row transition metal-based MRI probes.Chemical science · 2025Review
- Role of Electron Correlation beyond the Active Space in Achieving Quantitative Predictions of Spin-Phonon Relaxation.Journal of chemical theory and computation · 2025Article
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2 authors.
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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.
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