mlsanityData to 7 Oct 2026

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

arXiv 2004.11658 ↗First posted 24 Apr 2020physics.comp-ph cs.CEcited by 15

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Abstract (arXiv)

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that the GPU version is 7 times faster than the CPU version with the same power consumption. The code can scale up to the entire Summit supercomputer. For a copper system of 113, 246, 208 atoms, the code can perform one nanosecond MD simulation per day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such unprecedented ability to perform MD simulation with ab initio accuracy opens up the possibility of studying many important issues in materials and molecules, such as heterogeneous catalysis, electrochemical cells, irradiation damage, crack propagation, and biochemical reactions.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Apr 2020.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 1; 2024: 0; 2025: 5; 2026: 1.

15 papers.

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  1. 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 516
  2. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  3. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  4. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  5. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
  6. 2022SchNetPack 2.0: A neural network toolbox for atomistic machine learningcited by 29
  7. 2022Partial Differential Equations Meet Deep Neural Networks: A Surveycited by 25
  8. 2025A practical guide to machine learning interatomic potentials -- Status and futurecited by 24
  9. 2021Deep Molecular Representation Learning via Fusing Physical and Chemical Informationcited by 21
  10. 2025chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulationscited by 6
  11. 2022A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kineticscited by 5
  12. 2025Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamicscited by 1
3 more
  1. 2025Synergizing chemical and AI communities for advancing laboratories of the futurecited by 1
  2. 2025Reciprocal Space Attention for Learning Long-Range Interactionscited by 0
  3. 2026Hessian-informed machine learning interatomic potential towards bridging theory and experimentscited by 0