Development of a machine learning potential for the study of crack propagation in titanium. (1st December 2021)

• Record Type: Journal Article
• Authors: Shen, Linjie; Wang, Yi; Lai, Wensheng
• Is Part Of: International journal of pressure vessels and piping. Volume 194:Part A(2021)
• Publication Date: 2021-12-01
• Subjects: Titanium -- Machine learning potential -- Molecular dynamics simulation -- Crack; Pressure vessels -- Periodicals; Pipe -- Periodicals; Récipients sous pression -- Périodiques; Tuyaux -- Périodiques; Pipe; Pressure vessels; Periodicals; 681.76041
• DOI: 10.1016/j.ijpvp.2021.104514
• Languages: English
• ISSNs: 0308-0161

Abstract

Abstract: In this paper, a machine learning potential of titanium in the form of moment tensor potential (MTP) is constructed to study the crack propagation behavior of titanium with a pre-crack via molecular dynamics (MD) simulations. The MTP of titanium is obtained by learning the results of the first-principles calculation for various configurations, and is verified to rationally describe the statistic and dynamic property of titanium. Using MTP, MD simulations of a pre-crack on { 10 1 ‾ 0 } and { 0001 } planes upon loading are performed. The results show that generation of twinning occurs in the { 10 1 ‾ 0 } plane, while generation of partial dislocation occurs in the { 0001 } plane. MD simulations of a preset microcrack using traditional potential function are also presented and compared with those with MTP. Highlights: A machine learning potential of titanium in the form of moment tensor potential (MTP) is presented. The crack propagation behavior of titanium with a pre-crack is studied via molecular dynamics simulations with MTP.