Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians

Accurate nonadiabatic molecular dynamics (NAMD) is crucial for studying excited-state dynamics in solids but is computationally expensive. Here, authors use machine learning to enhance the efficiency and accuracy of NAMD simulations in solids.