A neural-network Hamiltonian enables true on-the-fly nonadiabatic molecular dynamics in solids, replacing the ground-state classical-path approximation with excited-state forces and couplings.
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Nonadiabatic Molecular Dynamics on Real-time Excited-State Surfaces via Machine Learning Hamiltonians
A neural-network Hamiltonian enables true on-the-fly nonadiabatic molecular dynamics in solids, replacing the ground-state classical-path approximation with excited-state forces and couplings.