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

Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang

Research output: Contribution to journalArticlepeer-review

103 Scopus citations


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 for a water system of 12,582,912 atoms, the GPU version can be 7 times faster than the CPU version under 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. Program summary: Program Title: DeePMD-kit CPC Library link to program files: http://dx.doi.org/10.17632/phyn4kgsfx.1 Developer's repository link: http://dx.doi.org/10.5281/zenodo.3961106 Licensing provisions: LGPL Programming language: C++/Python/CUDA Journal reference of previous version: Comput. Phys. Commun. 228 (2018), 178–184. Does the new version supersede the previous version?: Yes. Reasons for the new version: Parallelize and optimize the DeePMD-kit for modern high performance computers. Summary of revisions: The optimized DeePMD-kit is capable of computing 100 million atoms molecular dynamics with ab initio accuracy, achieving 86 PFLOPS in double precision. Nature of problem: Modeling the many-body atomic interactions by deep neural network models. Running molecular dynamics simulations with the models. Solution method: The Deep Potential for Molecular Dynamics (DeePMD) method is implemented based on the deep learning framework TensorFlow. Standard and customized TensorFlow operators are optimized for GPU. Massively parallel molecular dynamics simulations with DeePMD models on high performance computers are supported in the new version.

Original languageEnglish (US)
Article number107624
JournalComputer Physics Communications
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • General Physics and Astronomy


  • DeePMD-kit
  • Deep potential
  • GPU
  • Heterogeneous architecture
  • Molecular dynamics


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