Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910246563217408 |
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| author | Brunken, Christoph Cormier, Titouan Walewski, Lucien Carobene, Marco Khanfir, Yessine Weller-Davies, Zachary Bragança, Miguel Picard, Armand Pichard, Adrien Wehrhan, Leon Chomet, Heloise Varga-Umbrich, Eszter Bluntzer, Marie Bortone, Massimo Heyraud, Valentin Acosta-Gutiérrez, Silvia Tilly, Jules Peltre, Olivier |
| author_facet | Brunken, Christoph Cormier, Titouan Walewski, Lucien Carobene, Marco Khanfir, Yessine Weller-Davies, Zachary Bragança, Miguel Picard, Armand Pichard, Adrien Wehrhan, Leon Chomet, Heloise Varga-Umbrich, Eszter Bluntzer, Marie Bortone, Massimo Heyraud, Valentin Acosta-Gutiérrez, Silvia Tilly, Jules Peltre, Olivier |
| contents | Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is often limited by fragmented tooling, limited scalability, and inflexible software design. We present mlip v2, a new generation of the mlip library that advances efficient and scalable molecular simulation through a unified and extensible framework. The new release features a targeted API redesign with improved modularity and control, enabling flexible customization of training, data processing, and simulation workflows. It further integrates a new high-performance backend for equivariant operations, e3j, significantly accelerating model inference and simulations. In addition, the framework introduces a range of entirely new capabilities, including the eSEN architecture with a Mixture-of-Experts formulation for scalable training on large and diverse datasets, improved handling of electrostatics through more physically grounded charge modeling and long-range interaction treatment, and advanced simulation features such as NPT ensembles and nudged elastic band methods. Together, these extensions significantly broaden the scope of MLIP applications, enabling efficient modeling of complex, reactive, and out-of-equilibrium systems, and bridging the gap between ML research and practical molecular simulation applications. The library is available on GitHub and on PyPI under the Apache license 2.0. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22698 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Brunken, Christoph Cormier, Titouan Walewski, Lucien Carobene, Marco Khanfir, Yessine Weller-Davies, Zachary Bragança, Miguel Picard, Armand Pichard, Adrien Wehrhan, Leon Chomet, Heloise Varga-Umbrich, Eszter Bluntzer, Marie Bortone, Massimo Heyraud, Valentin Acosta-Gutiérrez, Silvia Tilly, Jules Peltre, Olivier Chemical Physics Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is often limited by fragmented tooling, limited scalability, and inflexible software design. We present mlip v2, a new generation of the mlip library that advances efficient and scalable molecular simulation through a unified and extensible framework. The new release features a targeted API redesign with improved modularity and control, enabling flexible customization of training, data processing, and simulation workflows. It further integrates a new high-performance backend for equivariant operations, e3j, significantly accelerating model inference and simulations. In addition, the framework introduces a range of entirely new capabilities, including the eSEN architecture with a Mixture-of-Experts formulation for scalable training on large and diverse datasets, improved handling of electrostatics through more physically grounded charge modeling and long-range interaction treatment, and advanced simulation features such as NPT ensembles and nudged elastic band methods. Together, these extensions significantly broaden the scope of MLIP applications, enabling efficient modeling of complex, reactive, and out-of-equilibrium systems, and bridging the gap between ML research and practical molecular simulation applications. The library is available on GitHub and on PyPI under the Apache license 2.0. |
| title | Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2605.22698 |