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

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.