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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 us 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 (graphics processing units). We hope that FeNNol will facilitate the development and application of new hybrid NNP architectures for a wide range of molecular simulation problems.<\/jats:p>","DOI":"10.1063\/5.0217688","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T12:15:25Z","timestamp":1721909725000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":15,"title":["FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials"],"prefix":"10.1063","volume":"161","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0035-3255","authenticated-orcid":false,"given":"Thomas","family":"Pl\u00e9","sequence":"first","affiliation":[{"name":"Sorbonne Universit\u00e9, LCT, UMR 7616 CNRS , 75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Adjoua","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, LCT, UMR 7616 CNRS , 75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7251-0910","authenticated-orcid":false,"given":"Louis","family":"Lagard\u00e8re","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, LCT, UMR 7616 CNRS , 75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6615-9426","authenticated-orcid":false,"given":"Jean-Philip","family":"Piquemal","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, LCT, UMR 7616 CNRS , 75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"2024072512151197800_c1","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1002\/jcc.20035","volume":"25","year":"2004","journal-title":"J.\u00a0Comput. 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