超级计算机
计算机科学
从头算
分子动力学
计算科学
双精度浮点格式
从头算量子化学方法
物理
人工智能
并行计算
算法
计算化学
化学
浮点型
量子力学
分子
作者
Weile Jia,Han Wang,Mohan Chen,Denghui Lu,Lin Lin,Roberto Car,E Weinan,Linfeng Zhang
出处
期刊:IEEE International Conference on High Performance Computing, Data, and Analytics
日期:2020-11-01
被引量:78
标识
DOI:10.1109/sc41405.2020.00009
摘要
For 35 years, 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 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 ab initio accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling.
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