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arxiv: 2505.22397 · v2 · pith:TTYVCQSE · submitted 2025-05-28 · physics.chem-ph

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems

Christoph Brunken , Olivier Peltre , Heloise Chomet , Lucien Walewski , Manus McAuliffe , Valentin Heyraud , Solal Attias , Martin Maarand
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Yessine Khanfir Edan Toledo Fabio Falcioni Marie Bluntzer Silvia Acosta-Guti\'errez Jules Tilly
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classification physics.chem-ph
keywords learninglibrarymachinemlipmolecularefficientmodelsdynamics
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Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT). In this white paper, we present our MLIP library which was created with two core aims: (1) provide to industry experts without machine learning background a user-friendly and computationally efficient set of tools to experiment with MLIP models, (2) provide machine learning developers a framework to develop novel approaches fully integrated with molecular dynamics tools. The library includes in this release three model architectures (MACE, NequIP, and ViSNet), and two molecular dynamics (MD) wrappers (ASE, and JAX-MD), along with a set of pre-trained organics models. The seamless integration with JAX-MD, in particular, facilitates highly efficient MD simulations, bringing MLIP models significantly closer to industrial application. The library is available on GitHub and on PyPI under the Apache license 2.0.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs

    cs.LG 2026-05 unverdicted novelty 7.0

    Force-aware NTKs and chunked acquisition enable scalable, robust active learning for MLIPs, achieving lowest energy and force errors on OC20 and remaining competitive on other benchmarks.

  2. Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs

    cs.LG 2026-05 unverdicted novelty 7.0

    Pretrained MLIP latent spaces yield NTK and activation kernels that outperform standard acquisition functions in active learning for reactive MLIP training, reducing required labels by 38% for energy and 28% for force errors.

  3. Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs

    cs.LG 2026-05 unverdicted novelty 7.0

    Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.

  4. Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs

    cs.LG 2026-05 unverdicted novelty 6.0

    Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.

  5. Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation

    physics.chem-ph 2026-05 unverdicted novelty 4.0

    mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.