Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning

@article{Jia2020PushingTL,
  title={Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning},
  author={Weile Jia and Han Wang and Mohan Chen and Denghui Lu and Jiduan Liu and Lin Lin and Roberto Car and E Weinan and Linfeng Zhang},
  journal={SC20: International Conference for High Performance Computing, Networking, Storage and Analysis},
  year={2020},
  pages={1-14}
}
  • Weile Jia, Han Wang, Linfeng Zhang
  • Published 1 May 2020
  • Computer Science
  • SC20: International Conference for High Performance Computing, Networking, Storage and Analysis
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 learningbased 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… 

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