Machine learning-driven prediction of ultrafast spin relaxation in metal halide perovskites for spintronic applications

Abstract

Spintronics can reduce the energy consumption of electronic devices. Perovskites have recently emerged as promising spintronic materials. The design of perovskite spintronic devices relies on the understanding and control of spin relaxation processes, which remain challenging. Here, we report a lightweight predictive model for the ultrafast spin relaxation rate based on a set of intrinsic descriptors that are either quantum-chemically computable or directly derivable from molecular composition. The model, constructed using an artificial neural network (ANN) and trained on a curated dataset of 52 perovskite materials (including 14 synthesised and characterised compounds investigated with ultrafast spin dynamics measurements), achieves high predictive accuracy with R² = 0.99 under leave-one-out cross-validation. SHapley Additive exPlanations (SHAP)-based interpretability analysis further reveals clear physical correlations between the spin relaxation rate and frontier orbital energies (HOMO and LUMO), molecular weight, polarizability, and dipole moment, clarifying how modulation of spin–orbit coupling, phonon scattering, and electron–hole exchange pathways act as primary mechanisms governing spin decoherence. This work establishes a generalizable and physically interpretable pre-synthetic design strategy for the rational development of spin-functional perovskite materials.