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Development of a machine learning potential for the study of crack propagation in titanium
International Journal of Pressure Vessels and Piping ( IF 3.0 ) Pub Date : 2021-08-04 , DOI: 10.1016/j.ijpvp.2021.104514
Linjie Shen 1 , Yi Wang 1, 2 , Wensheng Lai 1
Affiliation  

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 {1010} and {0001} planes upon loading are performed. The results show that generation of twinning occurs in the {1010} 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.



中文翻译:

开发用于研究钛裂纹扩展的机器学习潜力

在本文中,以矩张量势 (MTP) 形式构建了钛的机器学习势,以通过分子动力学 (MD) 模拟研究具有预裂纹的钛的裂纹扩展行为。钛的MTP是通过学习各种构型的第一性原理计算结果而得到的,并被验证可以合理地描述钛的统计和动态特性。使用 MTP、MD 模拟预裂纹{1010}{0001}执行加载时的平面。结果表明孪生的产生发生在{1010} 平面,而局部位错的产生发生在 {0001}飞机。还介绍了使用传统势函数对预设微裂纹的 MD 模拟,并与 MTP 进行了比较。

更新日期:2021-09-09
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