BlackDynamite

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials

arXiv:2601.01185v2 Announce Type: replace Abstract: Machine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum accuracy, it is necessary that neighborhoods contained in the training set are rather close to the ones encountered during a simulation. Yet, constructing a single training set that works well for all applications is, and likely will remain, infeasible, so, one...