From Structure Mining to Unsupervised Exploration of Atomic Octahedral Networks

Understanding the spatial arrangements of atom-centered
coordination
octahedra is crucial for relating structures to properties for many
materials families. Traditional case-by-case inspection becomes a
prohibitive task for discovering trends and similarities in large
data sets. Here, we operationalize chemical intuition to automate
the geometric parsing, quantification, and classification of coordination
octahedral networks using unsupervised machine learning. We apply
the workflow to analyze two data sets to demonstrate its effectiveness.
For computationally generated single oxide perovskite (ABO3) polymorphs, we uncover axis-dependent tilting trends that assist
in detecting oxidation state changes. For hybrid iodoplumbates (AxPbyIz) from measured structures, we taxonomize their octahedral
networks, revealing a Pauling-like connectivity rule for the coordination
environment and the design principles underpinning their structural
diversity. Our results offer a glimpse into the vast design space
of atomic octahedral networks in materials chemistry and inform high-throughput,
targeted screening of specific structure types.