Neural network potential for Zr–Rh system by machine learning. (25th November 2021)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Journal of physics. Volume 34:Number 7(2022)
• Publication Date: 2021-11-25
• Subjects: Zr–Rh metallic glass -- neural network potential -- machine learning -- molecular dynamics; Condensed matter -- Periodicals; Matière condensée -- Périodiques; Vaste stoffen; Vloeistoffen; Natuurkunde; Electronic journals; Computer network resources; 530.4105
• DOI: 10.1088/1361-648X/ac37dc
• Languages: English
• ISSNs: 0953-8984

Abstract

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. 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 Zr77 Rh23 . Our study lays a foundation for exploring the complex structures in amorphous Zr77 Rh23, which is of great significance for the design and practical application.