Neural network potential for Zr–Rh system by machine learning

Xie, Kun; Qiao, Chong; Shen, Hong; Yang, Riyi; Xu, Ming; Zhang, Chao; Zheng, Yuxiang; Zhang, Rongjun; Chen, Liangyao; Ho, Kai-Ming; Wang, Cai-Zhuang; Wang, Songyou

doi:10.1088/1361-648x/ac37dc

Title: Neural network potential for Zr–Rh system by machine learning

Journal Article · Thu Nov 25 00:00:00 EST 2021 · Journal of Physics. Condensed Matter

DOI:https://doi.org/10.1088/1361-648x/ac37dc· OSTI ID:1833051

Xie, Kun ^[1];

^[2];

^[1]; Yang, Riyi ^[1];

^[2]; Zhang, Chao ^[3]; Zheng, Yuxiang ^[1];

^[1]; Chen, Liangyao ^[1]; Ho, Kai-Ming ^[4];

^[4];

^[1]

Fudan Univ., Shanghai (China)
Huazhong Univ. of Science and Technology, Wuhan (China)
Yantai Univ. (China)
Ames Lab., and Iowa State Univ., Ames, IA (United States)

Zr–Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr–Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. Here, the results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr₇₇Rh₂₃. Our study lays a foundation for exploring the complex structures in amorphous Zr₇₇Rh₂₃, which is of great significance for the design and practical application.

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Research Organization:: Ames Lab., Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; Shanghai Municipal Science and Technology Commission; National Natural Science Foundation of China (NSFC); China Postdoctoral Science Foundation; Fundamental Research Funds for the Central Universities; National Key R&D Plan of China; Natural Science Foundation of Shandong Province

Grant/Contract Number:: AC02-07CH11358; 11874318; 18JC1411500; 11374055; 61427815; 51772113; 2020M682387; 2021GCRC051; 2017YFB0701701; ZR2018MA043

OSTI ID:: 1833051

Report Number(s):: IS-J-10,646; TRN: US2216807

Journal Information:: Journal of Physics. Condensed Matter, Vol. 34, Issue 7; ISSN 0953-8984

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
Zr-Rh metallic glass
neural network potential
machine learning
molecular dynamics

Title: Neural network potential for Zr–Rh system by machine learning

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