ArticleJournal of chemical theory and computation2025
chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations.
Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules.RSC advances · 2026Article
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.Journal of chemical information and modeling · 2026Article
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Learning non-local molecular interactions via equivariant local representations and charge equilibration.npj computational materials · 2025Article
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Authors and funding
4 authors.
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Abstract
Machine Learning Potentials (MLPs) have advanced rapidly and show great promise to transform Molecular Dynamics (MD) simulations. However, most existing software tools are tied to specific MLP architectures, lack integration with standard MD packages, or are not parallelizable across GPUs. To address these challenges, we present chemtrain-deploy, a framework that enables the model-agnostic deployment of MLPs in LAMMPS. chemtrain-deploy supports any JAX-defined semilocal potential, allowing users to exploit the functionality of LAMMPS and perform large-scale MLP-based MD simulations on multiple GPUs. It achieves state-of-the-art efficiency and scales to systems containing millions of atoms. We validate its performance and scalability using graph neural network architectures, including MACE, Allegro, and PaiNN, applied to a variety of systems such as liquid-vapor interfaces, crystalline materials, and solvated peptides. Our results highlight the practical utility of chemtrain-deploy for real-world, high-performance simulations and provide guidance for MLP architecture selection and future design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.