mlsanityData to 7 Oct 2026

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations

arXiv 2402.17660 ↗First posted 27 Feb 2024cs.LG physics.bio-ph physics.chem-phcited by 15

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Abstract (arXiv)

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2-fold to 10-fold over previous iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and the smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 29 of its 70 references that are in the map. First posted on arXiv 27 Feb 2024.

Builds on

29 of 70 references.

References · 29

  1. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  2. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  3. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  4. 2021E(n) Equivariant Graph Neural Networkscited by 951
  5. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  6. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
  7. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  8. 2019Cormorant: Covariant Molecular Neural Networkscited by 262
  9. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  10. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  11. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  12. 2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 225
17 more
  1. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  2. 2022e3nn: Euclidean Neural Networkscited by 171
  3. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  4. 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 92
  5. 2023On the Expressive Power of Geometric Graph Neural Networkscited by 89
  6. 2018Less is more: sampling chemical space with active learningcited by 80
  7. 2022SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentialscited by 80
  8. 2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 65
  9. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  10. 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentialscited by 53
  11. 2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
  12. 2022SchNetPack 2.0: A neural network toolbox for atomistic machine learningcited by 29
  13. 2023EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulationscited by 15
  14. 2016Optimizing Performance of Recurrent Neural Networks on GPUscited by 12
  15. 2023Data efficiency and extrapolation trends in neural network interatomic potentialscited by 10
  16. 2013Optimizing CUDA Code By Kernel Fusion---Application on BLAScited by 7
  17. 2013Accelerating Dissipative Particle Dynamics Simulations on GPUs: Algorithms, Numerics and Applicationscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 8.

15 papers.

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  1. 2024The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domainscited by 40
  2. 2026Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamicscited by 5
  3. 2024Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coveragecited by 3
  4. 2024AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamicscited by 3
  5. 2025Assessing generative modeling approaches for free energy estimates in condensed mattercited by 1
  6. 2026Boltzmann Generators for Condensed Matter via Riemannian Flow Matchingcited by 1
  7. 2024Implicit Delta Learning of High Fidelity Neural Network Potentialscited by 0
  8. 2025A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attentioncited by 0
  9. 2025Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigmcited by 0
  10. 2026AceFF: A State-of-the-Art Machine Learning Potential for Small Moleculescited by 0
  11. 2026FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamicscited by 0
  12. 2026Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACScited by 0
3 more
  1. 2026Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamicscited by 0
  2. 2026Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programmingcited by 0
  3. 2026The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learningcited by 0