From structure mining to unsupervised exploration of atomic octahedral networks

ArXi:2306.12272v2 Announce Type: replace-cross 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 datasets. 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 datasets to nstrate its effectiveness.