  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.
