  For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method
of choice for modeling complex atomistic phenomena from first principles.
However, most AIMD applications are limited by computational cost to systems
with thousands of atoms at most. We report that a machine learning-based
simulation protocol (Deep Potential Molecular Dynamics), while retaining {\it
ab initio} accuracy, can simulate more than 1 nanosecond-long trajectory of
over 100 million atoms per day, using a highly optimized code (GPU DeePMD-kit)
on the Summit supercomputer. Our code can efficiently scale up to the entire
Summit supercomputer, attaining $91$ PFLOPS in double precision ($45.5\%$ of
the peak) and {$162$/$275$ PFLOPS in mixed-single/half precision}. The great
accomplishment of this work is that it opens the door to simulating
unprecedented size and time scales with {\it ab initio} accuracy. It also poses
new challenges to the next-generation supercomputer for a better integration of
machine learning and physical modeling.
