Nonadiabatic Excited-State Dynamics with Quantum Monte Carlo-Trained Machine Learning: Azomethane as a Stringent Test

Abstract

We introduce quantum Monte Carlo (QMC)-trained multistate machine-learned (ML) force fields for nonadiabatic excited-state dynamics, targeting photochemical processes in which the electronic character changes along the reaction path and a consistent correlated description is required. In this framework, variational Monte Carlo (VMC) wave functions combine compact selected configuration-interaction (CIPSI) expansions with a Jastrow factor that explicitly accounts for dynamical correlation, while neural networks convert the stochastic VMC/CIPSI data into smooth potential energy surfaces for large surface-hopping ensembles. We apply this approach to azomethane, a demanding test case involving torsional relaxation through conical-intersection regions and C–N bond dissociation on the hot ground state. Benchmark calculations support the accuracy of the QMC reference data and show robust force convergence across isomerization and dissociation geometries. The QMC-trained dynamics preserves the expected photoisomerization mechanism, strongly reduces the excessive C–N breaking obtained with complete active space self-consistent field, and predicts a small but non-negligible prompt dissociation component after internal conversion, with a time scale consistent with femtosecond-resolved mass-spectrometry experiments. These results establish ML-QMC as a practical route to nonadiabatic photochemical dynamics with accurate wave function reference data.