期刊
ACTA MATERIALIA
卷 234, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actamat.2022.118006
关键词
Grain boundaries; Atomistic simulations; Aluminum
资金
- U.S. National Science Founda-tion (NSF) [DMR-1817321]
This study presents a computed dataset of 7304 unique aluminum grain boundaries in the 5D crystallographic space. The dataset includes a range of atomic configurations for each structure and has great potential for understanding the relationship between grain boundary structure and properties.
The space of possible grain boundary structures is vast, with 5 macroscopic, crystallographic degrees of freedom that define the character of a grain boundary. While numerous datasets of grain boundaries have examined this space in part or in full, we present a computed dataset of 7304 unique aluminum grain boundaries in the 5D crystallographic space. Our sampling also includes a range of possible microscopic, atomic configurations for each unique 5D crystallographic structure, which total over 43 million structures. We present the methods used to generate this dataset, an initial examination of the energy trends that follow the Read-Shockley relationship, hints at trends throughout the 5D space, variations in GB energy when non-minimum energy structures are examined, and insights gained in machine learning of grain boundary energy structure-property relationships. This dataset, which is available for download, has great potential for insight into GB structure-property relationships.(c) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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