Examination of computed aluminum grain boundary structures and energies that span the 5D space of crystallographic character
Examination of computed aluminum grain boundary structures and energies that span the 5D space of crystallographic character
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DOI：
10.1016/j.actamat.2022.118006
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发表时间：
2022-05-26
期刊：
影响因子：
9.4
通讯作者：
Serafin, Lydia Harris
中科院分区：
文献类型：
--
作者：
Homer, Eric R.;Hart, Gus L. W.;Serafin, Lydia Harris
可能的晶界结构的空间是巨大的，具有定义晶界特征的5个宏观晶体学自由度。虽然许多晶界数据集已经部分或全部研究了这个空间，但我们在5D晶体学空间中提供了7304个独特的铝晶界的计算数据集。我们的采样还包括每个独特的5D晶体结构的一系列可能的微观原子配置，总共超过4300万个结构。我们提出了用于生成该数据集的方法，遵循Read-Shockley关系的能量趋势的初步检查，提示整个5D空间的趋势，检查非最小能量结构时GB能量的变化，以及在晶界能量结构-性质关系的机器学习中获得的见解。该数据集可供下载，具有深入了解GB结构-性质关系的巨大潜力。(c)2022 Acta Materialia Inc.由爱思唯尔有限公司出版。保留所有权利。
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.