mlsanityData to 7 Oct 2026

TorchMD: A deep learning framework for molecular simulations

arXiv 2012.12106 ↗First posted 22 Dec 2020physics.chem-ph cs.AIcited by 55

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Abstract (arXiv)

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations including bond, angle, dihedral, Lennard-Jones and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab-initio potential, performing an end-to-end training and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool-set to support molecular simulations of machine learning potentials. Code and data are freely available at urlgithub.com/torchmd.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2020.

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Citing papers per year: 2021: 5; 2022: 13; 2023: 8; 2024: 9; 2025: 12; 2026: 8.

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  1. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  2. 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 92
  3. 2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
  4. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
  5. 2022SchNetPack 2.0: A neural network toolbox for atomistic machine learningcited by 29
  6. 2021Learning neural network potentials from experimental data via Differentiable Trajectory Reweightingcited by 26
  7. 2022Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networkscited by 23
  8. 2025Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functionalcited by 18
  9. 2023Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentialscited by 16
  10. 2024TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulationscited by 15
  11. 2023Accurate machine learning force fields via experimental and simulation data fusioncited by 12
  12. 2024chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physicscited by 12
38 more
  1. 2021Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulationscited by 8
  2. 2022NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechaniccited by 8
  3. 2022Differentiable Matrix Elements with MadJaxcited by 8
  4. 2023Differentiable Simulations for Enhanced Sampling of Rare Eventscited by 7
  5. 2022Thermodynamics-inspired Explanations of Artificial Intelligencecited by 6
  6. 2025chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulationscited by 6
  7. 2023Learning Interatomic Potentials at Multiple Scalescited by 5
  8. 2024A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamicscited by 5
  9. 2025Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentialscited by 5
  10. 2022Learning Pair Potentials using Differentiable Simulationscited by 4
  11. 2023Top-down machine learning of coarse-grained protein force-fieldscited by 4
  12. 2023Self-Tuning Hamiltonian Monte Carlo for Accelerated Samplingcited by 4
  13. 2025Equivariant Masked Position Prediction for Efficient Molecular Representationcited by 4
  14. 2025MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discoverycited by 4
  15. 2024Molecule Graph Networks with Many-body Equivariant Interactionscited by 3
  16. 2024On the design space between molecular mechanics and machine learning force fieldscited by 3
  17. 2024AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamicscited by 3
  18. 2022Automatic Identification of Chemical Moietiescited by 2
  19. 2022From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learningcited by 2
  20. 2022ImmunoLingo: Linguistics-based formalization of the antibody languagecited by 2
  21. 2025Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulationscited by 2
  22. 2021Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamicscited by 1
  23. 2025QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentialscited by 1
  24. 2025Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identificationcited by 1
  25. 2025Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecturecited by 1
  26. 2021Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platformscited by 0
  27. 2022Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscositycited by 0
  28. 2023Differentiable Rotamer Sampling with Molecular Force Fieldscited by 0
  29. 2023Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policiescited by 0
  30. 2024Pretraining Strategy for Neural Potentialscited by 0
  31. 2024it Asparagus: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfacescited by 0
  32. 2024OpenQDC: Open Quantum Data Commonscited by 0
  33. 2025Generative diffusion model surrogates for mechanistic agent-based biological modelscited by 0
  34. 2025Learning task-specific predictive models for scientific computingcited by 0
  35. 2025Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigmcited by 0
  36. 2026AceFF: A State-of-the-Art Machine Learning Potential for Small Moleculescited by 0
  37. 2026Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACScited by 0
  38. 2026Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulatorscited by 0

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