Vibrationally informed ML potentials: Optimizing interatomic force fields using simulated neutron scattering functions

Recent advances in ab initio molecular dynamics (AIMD) have enabled precise simulations of vibrational dynamics in molecular systems; however, the high computational cost of AIMD limits its application to small-scale systems and short time spans. Machine learning interatomic potentials (MLIPs) offer a promising route to extend these simulations. Training of MLIPs, however, is usually nontrivial, especially when attempting to capture both the structural and vibrational dynamics of molecular systems. In this paper, we introduce a multi-stage workflow that combines AIMD data from diverse simulation packages to simultaneously train multiple deep-learning models such as DeePMD-kit, NequIP, and Allegro. The framework employs a genetic algorithm for hyperparameter optimization and utilizes inelastic neutron scattering spectra as an additional performance metric, ensuring both the static structure and dynamic behavior are accurately reproduced. This integrated approach not only enhances the reliability of MLIPs in capturing complex interatomic interactions but also paves the way for more predictive and efficient materials modeling.