  Grain boundaries (GBs) govern critical properties of polycrystals. Although
significant advancements have been made in characterizing minimum energy GBs,
real GBs are seldom found in such states, making it challenging to establish
structure-property relationships. This diversity of atomic arrangements in
metastable states motivates using data-driven methods to establish these
relationships. In this study, we utilize a vast atomistic database (~5000) of
minimum energy and metastable states of symmetric tilt copper GBs, combined
with physically-motivated local atomic environment (LAE) descriptors (Strain
Functional Descriptors, SFDs) to predict GB properties. Our regression models
exhibit robust predictive capabilities using only 19 descriptors, generalizing
to atomic environments in nanocrystals. A significant highlight of our work is
integration of an unsupervised method with SFDs to elucidate LAEs at GBs and
their role in determining properties. Our research underscores the role of a
physics-based representation of LAEs and efficacy of data-driven methods in
establishing GB structure-property relationships.
