  Machine learning models and applications in materials design and discovery
typically involve the use of feature representations or "descriptors" followed
by a learning algorithm that maps them to a user-desired property of interest.
Most popular mathematical formulation-based descriptors are not unique across
atomic environments or suffer from transferability issues across different
application domains and/or material classes. In this work, we introduce the
Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph
attention-based architecture to learn unique feature representations and
perform classification of materials across multiple scales (from atomic to
mesoscale) and diverse classes ranging from metals, oxides, non-metals and even
hierarchical materials such as zeolites and semi ordered materials such as
mesophases. We first demonstrate a case study where the classification is based
on a global, structure-level representation such as space group and structural
dimensionality (e.g., bulk, 2D, clusters etc.). Using representative materials
such as polycrystals and zeolites, we next demonstrate the transferability of
our network in successfully performing local atom-level classification tasks,
such as grain boundary identification and other heterointerfaces. We also
demonstrate classification in (thermal) noisy dynamical environments using a
representative example of crystal nucleation and growth of a zeolite polymorph
from an amorphous synthesis mixture. Finally, we characterize the formation of
a binary mesophase and its phase transitions and the growth of ice,
demonstrating the performance of CEGANN in systems with thermal noise and
compositional diversity. Overall, our approach is agnostic to the material type
and allows for multiscale classification of features ranging from atomic-scale
crystal structures to heterointerfaces to microscale grain boundaries.
