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FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
May 3, 2024, 4:54 a.m. | Thomas Pl\'e, Olivier Adjoua, Louis Lagard\`ere, Jean-Philip Piquemal
cs.LG updates on arXiv.org arxiv.org
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 …
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