posted on 2022-01-06, 21:14authored byKazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh
While traditional
crystallographic representations of structure
play an important role in materials science, they are unsuitable for
efficient machine learning. A range of effective numerical descriptors
have been developed for molecular and crystal structures. We are interested
in a special case, where distortions emerge relative to an ideal high-symmetry
parent structure. We demonstrate that irreducible representations
form an efficient basis for the featurization of polyhedral deformations
with respect to such an aristotype. Applied to a data set of 552 octahedra
in ABO3 perovskite-type materials, we use unsupervised
machine learning with irreducible representation descriptors to identify
four distinct classes of behaviors, associated with predominately
corner, edge, face, and mixed connectivity between neighboring octahedral
units. Through this analysis, we identify SrCrO3 as a material
with tunable multiferroic behavior. We further show, through supervised
machine learning, that thermally activated structural distortions
of CsPbI3 are well described by this approach.