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（NNP）已被证明是准确模拟复杂分子系统同时规避从头算分子动力学模拟高计算成本的有效工具。近年来，模型架构的诸多进展以及将机器学习（ML）与传统物理启发式力场相互作用相结合的混合模型开发，显著扩展了ML势的设计空间。本文介绍FeNNol这一用于构建、训练和运行力场增强神经网络势的新型库。它提供灵活模块化的混合模型构建系统，可在无需显式编程的前提下，轻松将最先进的嵌入表示与ML参数化的物理相互作用项相结合。此外，FeNNol利用Jax Python库的自动微分和即时编译特性实现NNP的快速评估，缩小了ML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（图形处理器）上模拟速度几乎与AMOEBA极化力场相当。我们期望FeNNol能促进面向广泛分子模拟问题的新型混合NNP架构的开发与应用。