mlsanityData to 7 Oct 2026

Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks

arXiv 2204.10348 ↗First posted 21 Apr 2022cs.LG physics.comp-phcited by 23

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Abstract (arXiv)

Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive. Learning-based force fields have made significant progress in accelerating ab-initio MD simulation but are not fast enough for many real-world applications due to slow inference for large systems and small time steps (femtosecond-level). We aim to address these challenges by learning a multi-scale graph neural network that directly simulates coarse-grained MD with a very large time step (nanosecond-level) and a novel refinement module based on diffusion models to mitigate simulation instability. The effectiveness of our method is demonstrated in two complex systems: single-chain coarse-grained polymers and multi-component Li-ion polymer electrolytes. For evaluation, we simulate trajectories much longer than the training trajectories for systems with different chemical compositions that the model is not trained on. Structural and dynamical properties can be accurately recovered at several orders of magnitude higher speed than classical force fields by getting out of the femtosecond regime.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 29 of its 85 references that are in the map. First posted on arXiv 21 Apr 2022.

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29 of 85 references.

References · 29

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  4. 2018Neural Ordinary Differential Equationscited by 3,733
  5. 2021E(n) Equivariant Graph Neural Networkscited by 951
  6. 2020Learning to Simulate Complex Physics with Graph Networkscited by 867
  7. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  8. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  9. 2020Directional Message Passing for Molecular Graphscited by 556
  10. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  11. 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 213
  12. 2020Machine Learning Force Fieldscited by 204
17 more
  1. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  2. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  3. 2017VAMPnets: Deep learning of molecular kineticscited by 135
  4. 2019Machine learning for molecular simulationcited by 109
  5. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  6. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 68
  7. 2020Coarse Graining Molecular Dynamics with Graph Neural Networkscited by 56
  8. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  9. 2017Variational approach for learning Markov processes from time series datacited by 51
  10. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
  11. 2018Coarse-Graining Auto-Encoders for Molecular Dynamicscited by 30
  12. 2018Deep Generative Markov State Modelscited by 19
  13. 2020Molecular Latent Space Simulatorscited by 13
  14. 2021Physics-aware, probabilistic model order reduction with guaranteed stabilitycited by 12
  15. 2018Transferable neural networks for enhanced sampling of protein dynamicscited by 9
  16. 2020Graph Neural Network Based Coarse-Grained Mapping Predictioncited by 4
  17. 2021Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated propertiescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 13; 2026: 3.

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  1. 2023Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamicscited by 62
  2. 2024Generative Modeling of Molecular Dynamics Trajectoriescited by 48
  3. 2025Scalable Equilibrium Sampling with Sequential Boltzmann Generatorscited by 36
  4. 2025Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systemscited by 21
  5. 2024EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolantscited by 15
  6. 2025Force-Free Molecular Dynamics Through Autoregressive Equivariant Networkscited by 13
  7. 2025FlashMD: long-stride, universal prediction of molecular dynamicscited by 11
  8. 2025Amortized Sampling with Transferable Normalizing Flowscited by 11
  9. 2024Scalable Normalizing Flows Enable Boltzmann Generators for Macromoleculescited by 10
  10. 2024Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materialscited by 9
  11. 2025UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomoleculescited by 7
  12. 2024Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptidescited by 6
11 more
  1. 2025MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Modelscited by 6
  2. 2024Learning Macroscopic Dynamics from Partial Microscopic Observationscited by 3
  3. 2025Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systemscited by 3
  4. 2025Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentialscited by 3
  5. 2026Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operatorscited by 3
  6. 2025Improving the Stability of GNN Force Field Models by Reducing Feature Correlationcited by 0
  7. 2025Natively Trainable Sparse Attention for Hierarchical Point Cloud Datasetscited by 0
  8. 2025Generative Quasi-Continuum Modeling of Confined Fluids at the Nanoscalecited by 0
  9. 2025Scalable learning of macroscopic stochastic dynamicscited by 0
  10. 2026Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictorcited by 0
  11. 2026MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perceptioncited by 0

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  1. 2023Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model
  2. 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
  3. 2026A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers
  4. 2024Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
  5. 2024Molecular relaxation by reverse diffusion with time step prediction
  6. 2025ToPolyAgent: AI Agents for Coarse-Grained Topological Polymer Simulations
  7. 2022Flow-matching -- efficient coarse-graining of molecular dynamics without forces
  8. 2025ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics