Development of a machine learning potential for the study of crack propagation in titanium. (1st December 2021) - Shen, Linjie; Wang, Yi; Lai, Wensheng
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.