mlsanityData to 7 Oct 2026

Graph Neural Networks Accelerated Molecular Dynamics

arXiv 2112.03383 ↗First posted 6 Dec 2021cs.LG physics.chem-phcited by 33

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Abstract (arXiv)

Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter. Since the resolution of MD is atomic-scale, achieving long time-scale simulations with femtosecond integration is very expensive. In each MD step, numerous iterative computations are performed to calculate energy based on different types of interaction and their corresponding spatial gradients. These repetitive computations can be learned and surrogated by a deep learning model like a Graph Neural Network (GNN). In this work, we developed a GNN Accelerated Molecular Dynamics (GAMD) model that directly predicts forces given the state of the system (atom positions, atom types), bypassing the evaluation of potential energy. By training the GNN on a variety of data sources (simulation data derived from classical MD and density functional theory), we show that GAMD can predict the dynamics of two typical molecular systems, Lennard-Jones system and Water system, in the NVT ensemble with velocities regulated by a thermostat. We further show that GAMD's learning and inference are agnostic to the scale, where it can scale to much larger systems at test time. We also perform a comprehensive benchmark test comparing our implementation of GAMD to production-level MD softwares, showing GAMD's competitive performance on the large-scale simulation.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 29 of its 93 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

29 of 93 references.

References · 29

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Layer Normalizationcited by 5,579
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  5. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  6. 2020Learning to Simulate Complex Physics with Graph Networkscited by 867
  7. 2020Learning Mesh-Based Simulation with Graph Networkscited by 840
  8. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  9. 2019Hamiltonian Neural Networkscited by 702
  10. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  11. 2020Directional Message Passing for Molecular Graphscited by 556
  12. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
17 more
  1. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  2. 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
  3. 2016Molecular Graph Convolutions: Moving Beyond Fingerprintscited by 238
  4. 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 213
  5. 2020Machine Learning Force Fieldscited by 204
  6. 2020Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Predictioncited by 137
  7. 2019Machine learning for molecular simulationcited by 109
  8. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  9. 2017DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamicscited by 79
  10. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 68
  11. 2021ForceNet: A Graph Neural Network for Large-Scale Quantum Calculationscited by 58
  12. 2020Coarse Graining Molecular Dynamics with Graph Neural Networkscited by 56
  13. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  14. 2017Hierarchical modeling of molecular energies using a deep neural networkcited by 37
  15. 2020Orbital Graph Convolutional Neural Network for Material Property Predictioncited by 29
  16. 2020Differentiable Molecular Simulations for Control and Learningcited by 25
  17. 2019Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systemscited by 12

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Citing papers per year: 2022: 7; 2023: 8; 2024: 5; 2025: 7; 2026: 6.

33 papers.

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  5. 2025FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Modelscited by 2
  6. 2026Super-resolution of turbulent reacting flows on complex meshes using graph neural networkscited by 2
  7. 2022Predicting CO2 Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networkscited by 1
  8. 2023Learning to simulate partially known spatio-temporal dynamics with trainable difference operatorscited by 1
  9. 2025Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discoverycited by 1
  10. 2026A panoramic aerodynamic performance prediction method for turbomachinery cascades using transformer-enhanced neural operatorcited by 1
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  13. 2024Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networkscited by 0
  14. 2024Generalizability of Graph Neural Network Force Fields for Predicting Solid-State Propertiescited by 0
  15. 2025MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assemblycited by 0
  16. 2025Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Modelscited by 0
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