Neural network potential for Zr-Rh system by machine learning

Abstract

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.This is a manuscript of an article published as Xie, Kun, Chong Qiao, Hong Shen, Riyi Yang, Ming Xu, Chao Zhang, Yuxiang Zheng et al. "Neural network potential for Zr–Rh system by machine learning." Journal of Physics: Condensed Matter 34, no. 7 (2021): 075402. DOI: 10.1088/1361-648X/ac37dc. Copyright 2021 IOP Publishing Ltd. DOE Contract Number(s): AC02-07CH11358; 11874318; 18JC1411500; 11374055; 61427815; 51772113; 2020M682387; 2021GCRC051; 2017YFB0701701; ZR2018MA043. Posted with permission