FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
arxiv(2024)
摘要
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 and running force-field-enhanced neural network
potentials. It provides a flexible and modular system for building hybrid
models, allowing to easily combine state-of-the-art embeddings with
ML-parameterized physical interaction terms without the need for explicit
programming. Furthermore, FeNNol leverages the automatic differentiation and
just-in-time compilation features of the Jax Python library to enable fast
evaluation of NNPs, shrinking the performance gap between ML potentials and
standard force-fields. This is demonstrated with the popular ANI-2x model
reaching simulation speeds nearly on par with the AMOEBA polarizable
force-field on commodity GPUs (GPU=Graphics processing unit). We hope that
FeNNol will facilitate the development and application of new hybrid NNP
architectures for a wide range of molecular simulation problems.
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