FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
<|reference_start|>FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials: 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.<|reference_end|>
@article{plé2024fennol:,
    title={FeNNol: an Efficient and Flexible Library for Building
  Force-field-enhanced Neural Network Potentials},
    author={Thomas Pl'e, Olivier Adjoua, Louis Lagard`ere, Jean-Philip Piquemal},
    journal={Journal of Chemical Physics, 2024, 161, 042502},
    year={2024},
    doi={10.1063/5.0217688},
    archivePrefix={arXiv},
    eprint={2405.01491},
    primaryClass={physics.chem-ph cs.LG}
}