  Obtaining microscopic structure-property relationships for grain boundaries
are challenging because of the complex atomic structures that underlie their
behavior. This has led to recent efforts to obtain these relationships with
machine learning, but representing a grain boundary structure in a manner
suitable for machine learning is not a trivial task. There are three key steps
common to property prediction in grain boundaries and other variable-sized atom
clustered structures. These are: (1) describe the atomic structure as a feature
matrix, (2) transform the variable-sized feature matrices of different
structures to a fixed length common to all structures, and (3) apply machine
learning to predict properties from the transformed feature matrices. We
examine these feature engineering steps to understand how they impact the
accuracy of grain boundary energy predictions. A database of over 7000 grain
boundaries serves to evaluate the different feature engineering combinations.
We also examine how these combination of engineered features provide
interpretability, or the ability to extract insightful physics from the
obtained structure-property relationships.
