FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials

arXiv:2405.01491v1 Announce Type: cross
Abstract: Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-initio molecular dynamics simulations. In recent years, numerous advances in model architectures as well as the development of hybrid models combining machine-learning (ML) with more traditional, physically-motivated, force-field interactions have considerably increased the design space of ML potentials. In this paper, we present FeNNol, a new library for building, training …

abstract advances architectures arxiv building cost cs.lg development dynamics library molecular dynamics network neural network numerical physics.chem-ph simulations systems tools type while