Adaptive Selection of Atomic Fingerprints for High-Dimensional Neural Network Potentials

Molecular dynamics simulations of solidification phenomena require accuraterepresentations of solid and liquid phases, making classical force fields oftenunsuitable. On the other hand ab initio simulations are infeasible to observe rarenucleation events. Being able to recreate ab initio quality forces, at scalability andefficiency near that of classical force fields, simulation of solidification processesis a promising area of application for machine-learned interatomic force fields.In a neural network potential the choice of input features plays a vital part in itsperformance. Here we propose embedded feature selection, using the adaptivegroup lasso technique, for identifying and removing irrelevant atomic fingerprints.