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TensorMD: Molecular Dynamics Simulation with Ab Initio Accuracy of 50 Billion Atoms

Yucheng Ouyang, Ying Liu, Honghui Shang, Zhenchuan Chen, Jiahao Shan,Huimin Cui,Xiaobing Feng,Xin Chen,Xingyu Gao, Lifang Wang,Haifeng Song,Rongfen Lin, Fang Li

ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming(2025)

Institute of Computing Technology

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Abstract
Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. This inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. This paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., ~ 3× compared with state-of-the-art.
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