From structure mining to unsupervised exploration of atomic octahedral networks
This work addresses the challenge of efficiently characterizing and classifying complex atomic networks in solid-state materials, which is crucial for materials discovery and property prediction, though it is incremental in automating existing chemical intuition.
The authors tackled the problem of automating the analysis of atomic octahedral networks in materials science, which is traditionally done manually and limits large-scale discovery. They developed a method that identified tilting trends in perovskites and uncovered violations of Pauling's third rule in hybrid iodoplumbates, enabling high-throughput screening of structure types.
Networks of atom-centered coordination octahedra commonly occur in inorganic and hybrid solid-state materials. Characterizing their spatial arrangements and characteristics is crucial for relating structures to properties for many materials families. The traditional method using case-by-case inspection becomes prohibitive for discovering trends and similarities in large datasets. Here, we operationalize chemical intuition to automate the geometric parsing, quantification, and classification of coordination octahedral networks. We find axis-resolved tilting trends in ABO$_{3}$ perovskite polymorphs, which assist in detecting oxidation state changes. Moreover, we develop a scale-invariant encoding scheme to represent these networks, which, combined with human-assisted unsupervised machine learning, allows us to taxonomize the inorganic framework polytypes in hybrid iodoplumbates (A$_x$Pb$_y$I$_z$). Consequently, we uncover a violation of Pauling's third rule and the design principles underpinning their topological diversity. Our results offer a glimpse into the vast design space of atomic octahedral networks and inform high-throughput, targeted screening of specific structure types.