Ultrafast Nonadiabatic Dynamics in 2D Perovskites assisted by Machine Learning

An exploration of the on-the-fly non-adiabatic couplings (NAC) for nonradiative relaxation and recombination of excited states in 2D Dion Jackobson Lead-halide perovskites is accelerated by a machime learning approach to ab initio molecular dynamics. Molecular dynamics of nanostructures composed of heavy elements is performed with use of machine learned force-fields (MLFF), as implemented in Vienna Ab initio Simulation Package (VASP). The force field parameterization is establised using on-the-fly learning, which continuously builds a force field using ab initio MD data. At each step of MD it is determined whether to perform an ab initio calculation or to use the force field and skip learning for that step using a Bayesian-learning algorithm. The total energy and forces are predicted based on the machine-learned force field at each time step of the MD simulation and if the Bayesian error estimate exceeds a threshold an ab initio calculation is performed. Model training and evaluation were performed for a range of for a 2D Dion-Jaconson lead halide perovskite models of different thickness and composition. The MLFF-MD trajectories were evaluated against AIMD trajectories to asses level of discrepancy and error accumulation. To examine the practical effectiveness of this approach we have used the MLFF-based MD trajectories to compute NAC and excited-state dynamics. At each stage, results based on machine learning are comapred to traditional ab initio-based electronic dissipative dynamics. We find that MLFF-MD provides comparable results to ab initio MDs when the MLFF is trained in a NpT ensemble.