Due to the complex potential energy surface, a requirement for large unit cells, long propagation times, and many trajectories needed for reliable statistics, we suggest that stateof-the-art simulations of short laser pulse-induced dynamics should follow our methodology. The first step is the construction of machine learning interatomic potential that should be trained on the configurations that sample well the configurational space explored in dynamics. The second step is to converge the integration time, cell size, and the number of needed trajectories. Once this is set, the final step is then to run Langevin molecular dynamics simulations with electronic friction and stochastic forces that depend on the twotemperature model.