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

• URL: http://arxiv.org/abs/2405.01491v2
• Date: Mon, 6 May 2024 15:45:46 GMT
• Title: FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
• Authors: Thomas Plé, Olivier Adjoua, Louis Lagardère, Jean-Philip Piquemal,
• Abstract summary: 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. It is demonstrated with the popular ANI-2x model reaching simulation speeds nearly on par with the AMOEBA polarizable force-field.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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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