mlsanityData to 7 Oct 2026

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv 2310.03121 ↗First posted 4 Oct 2023physics.chem-ph cs.LGcited by 44

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Abstract (arXiv)

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 9 of its 51 references that are in the map. First posted on arXiv 4 Oct 2023.

Builds on

9 of 51 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  3. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
  4. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  5. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  6. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  7. 2022SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentialscited by 80
  8. 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentialscited by 53
  9. 2022NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechaniccited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 17; 2026: 21.

44 papers.

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  2. 2024Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplerscited by 10
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  12. 2025Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spacescited by 4
32 more
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  2. 2025Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulationscited by 2
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  4. 2025Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Designcited by 2
  5. 2026Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularizationcited by 2
  6. 2026Unified Biomolecular Trajectory Generation via Pretrained Variational Bridgecited by 2
  7. 2026Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differencescited by 2
  8. 2026MDArena: Evaluating Coding Agents on Realistic Molecular Dynamics Workflowscited by 2
  9. 2024Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulationscited by 1
  10. 2024Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspectivecited by 1
  11. 2025InstructPro: Natural Language Guided Ligand-Binding Protein Designcited by 1
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  18. 2024Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactionscited by 0
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  20. 2025Active Learning for Machine Learning Driven Molecular Dynamicscited by 0
  21. 2025Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigmcited by 0
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  24. 2026CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimationcited by 0
  25. 2026All-atomistic Transferable Neural Potentials for Protein Solvationcited by 0
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  29. 2026Temperature transferable Machine Learned Coarse Grained model for proteinscited by 0
  30. 2026Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracycited by 0
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