Training one DeePMD Model in Minutes: a Step towards Online Learning

Author:

Hu Siyu12ORCID,Zhao Tong32ORCID,Sha Qiuchen32ORCID,Li Enji3ORCID,Meng Xiangyu4ORCID,Liu Liping52ORCID,Wang Lin-Wang52ORCID,Tan Guangming32ORCID,Jia Weile32ORCID

Affiliation:

1. State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, Beijing, China

2. University of Chinese Academy of Sciences, Beijing, China

3. State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

4. College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao, Shandong, China

5. Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

Publisher

ACM

Reference54 articles.

1. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. {TensorFlow}: a system for {Large-Scale} machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16). 265--283.

2. The assessment of defects in structures of strain hardening material

3. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

4. Representing potential energy surfaces by high-dimensional neural network potentials

5. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems

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