Examination of computed aluminum grain boundary structures and interface energies that span the 5D space of crystallographic character

TL;DR

This paper presents a comprehensive computational dataset of over 7,300 aluminum grain boundary structures across the 5D crystallographic space, including atomic configurations, and explores energy trends and machine learning insights.

Contribution

It introduces a large, detailed dataset of aluminum grain boundaries in 5D space, including atomic configurations, and analyzes energy trends and machine learning applications.

Findings

Energy trends follow the Read-Shockley relationship.

Variations in grain boundary energy when non-minimum structures are considered.

Insights into machine learning of grain boundary energy relationships.

Abstract

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 over 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 an overview of 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.