arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1707.09699cond-mat.mtrl-sci
收藏

基于进化搜索和机器学习的晶界相行为预测

Predicting phase behavior of grain boundaries with evolutionary search and machine learning

Qiang Zhu, Amit Samanta, Bingxi Li, Robert E. Rudd, Timofey Frolov

摘要:

晶界相变的研究是一个新兴的领域,直到最近才被实验所主导。探索这种现象的原子模型的主要瓶颈一直是缺乏一个强大的计算工具,可以预测接口结构。在这里,我们开发了一种新的计算工具,基于进化算法,进行高效的大正则晶界结构搜索,我们设计了一个聚类分析,自动识别不同的晶界相。它的应用程序的对称倾斜边界在铜的模型系统揭示了一个意想不到的丰富的多态性的晶界结构。我们发现新的基态和亚稳态通过探索不同的原子密度的结构。我们的研究结果表明,在整个取向差范围内的晶界有多个阶段,并表现出结构转变,这表明界面的相行为可能是一个普遍的现象。

英文摘要:

The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a new computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we design a clustering analysis that automatically identifies different grain boundary phases. Its application to a model system of symmetric tilt boundaries in Cu uncovers an unexpected rich polymorphism in the grain boundary structures. We find new ground and metastable states by exploring structures with different atomic densities. Our results demonstrate that the grain boundaries within the entire misorientation range have multiple phases and exhibit structural transitions, suggesting that phase behavior of interfaces is likely a general phenomenon.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑