期刊
PHYSICA B-CONDENSED MATTER
卷 532, 期 -, 页码 9-14出版社
ELSEVIER
DOI: 10.1016/j.physb.2017.05.019
关键词
Grain-boundary; Segregation; Dopant; Data mining; Genetic algorithm; Regression
资金
- Japan Science and Technology Agency-Precursory Research for Embryonic Science and Technology (JST-PRESTO), Japan
- Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) [25106003, 26249092]
- Grants-in-Aid for Scientific Research [25106003, 17H06094] Funding Source: KAKEN
Grain boundary segregation of dopants plays a crucial role in materials properties. To investigate the dopant segregation behavior at the grain boundary, an enormous number of combinations have to be considered in the segregation of multiple dopants at the complex grain boundary structures. Here, two data mining techniques, the random-forests regression and the genetic algorithm, were applied to determine stable segregation sites at grain boundaries efficiently. Using the random-forests method, a predictive model was constructed from 2% of the segregation configurations and it has been shown that this model could determine the stable segregation configurations. Furthermore, the genetic algorithm also successfully determined the most stable segregation configuration with great efficiency. We demonstrate that these approaches are quite effective to investigate the dopant segregation behaviors at grain boundaries.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据