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

Area of Science:

• Computational Physics
• Quantum Chemistry
• Materials Science

Background:

• Electron spin relaxation and decoherence are influenced by molecular and lattice vibrations.
• Understanding spin-phonon coupling is crucial, but traditional ab initio simulations are computationally expensive.
• High computational cost limits the progress in studying spin relaxation dynamics.

Purpose of the Study:

• To develop an accelerated computational framework for predicting molecular vibrations and spin-phonon coupling coefficients.
• To reduce the computational cost associated with studying spin-phonon interactions.
• To extend the framework for molecular dynamics simulations of spin relaxation.

Main Methods:

• Development of a machine-learning (ML) based computational framework.
• Prediction of molecular vibrations and spin-phonon coupling coefficients using ML models.
• Application of the ML framework to open-shell coordination compounds.

Main Results:

• The ML framework achieved semi-to-full quantitative agreement with traditional ab initio methods.
• Computational cost was reduced by approximately 80% compared to ab initio approaches.
• The framework demonstrated applicability to molecular dynamics simulations for spin relaxation studies.

Conclusions:

• The developed ML framework offers a computationally efficient alternative for studying spin-phonon coupling.
• This approach significantly lowers the barrier to investigating spin relaxation in complex systems.
• The framework opens new possibilities for exploring spin dynamics in condensed matter beyond equilibrium conditions.