Predicting
the properties of grain boundaries poses a challenge
because of the complex relationships between structural and chemical
attributes both at the atomic and continuum scales. Grain boundary
systems are typically characterized by parameters used to classify
local atomic arrangements in order to extract features such as grain
boundary energy or grain boundary strength. The present work utilizes
a combination of high-throughput atomistic simulations, macroscopic
and microscopic descriptors, and machine-learning techniques to characterize
the energy and strength of silicon carbide grain boundaries. A diverse
data set of symmetric tilt and twist grain boundaries are described
using macroscopic metrics such as misorientation, the alignment of
critical low-index planes, and the Schmid factor, but also in terms
of microscopic metrics, by quantifying the local atomic structure
and chemistry at the interface. These descriptors are used to create
random-forest regression models, allowing for their relative importance
to the grain boundary energy and decohesion stress to be better understood.
Results show that while the energetics of the grain boundary were
best described using the microscopic descriptors, the ability of the
macroscopic descriptors to reasonably predict grain boundaries with
low energy suggests a link between the crystallographic orientation
and the resultant atomic structure that forms at the grain boundary
within this regime. For grain boundary strength, neither microscopic
nor macroscopic descriptors were able to fully capture the response
individually. However, when both descriptor sets were utilized, the
decohesion stress of the grain boundary could be accurately predicted.
These results highlight the importance of considering both macroscopic
and microscopic factors when constructing constitutive models for
grain boundary systems, which has significant implications for both
understanding the fundamental mechanisms at work and the ability to
bridge length scales.