  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.
