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.

翻译：神经网络原子间势（NNPs）已被证明是精确模拟复杂分子系统同时绕过从头算分子动力学模拟高数值代价的有力工具。近年来，模型架构的众多进步以及将机器学习与更传统的物理驱动力场相结合的混合模型的发展，极大地扩展了机器学习势的设计空间。本文介绍了FeNNol，一个用于构建、训练和运行力场增强型神经网络势的新库。它提供了一种灵活且模块化的混合模型构建系统，允许轻松将最先进的嵌入与机器学习参数化的物理相互作用项结合，而无需显式编程。此外，FeNNol利用Jax Python库的自动微分和即时编译功能实现NNPs的快速评估，缩小了机器学习势与标准力场之间的性能差距。通过流行的ANI-2x模型验证，其在商用GPU上的模拟速度几乎与AMOEBA极化力场相当。我们期待FeNNol能够促进新型混合NNP架构的开发与应用，以解决广泛的分子模拟问题。