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

Denghui Lu, Han Wang, Mohan Chen, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang
CAPT, HEDPS, College of Engineering, Peking University, Beijing, China
arXiv:2004.11658 [physics.comp-ph], (29 Apr 2020)

@article{Jia_2019,

   title={Parallel transport time-dependent density functional theory calculations with hybrid functional on summit},

   ISBN={9781450362290},

   url={http://dx.doi.org/10.1145/3295500.3356144},

   DOI={10.1145/3295500.3356144},

   journal={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},

   publisher={ACM},

   author={Jia, Weile and Wang, Lin-Wang and Lin, Lin},

   year={2019},

   month={Nov}

}

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.
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