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 demonstrate its effectiveness. For computationally generated single oxide perovskite (ABO$_{3}$) polymorphs, we uncover axis-dependent tilting trends which assist in detecting oxidation state changes. For hybrid iodoplumbates (A$_x$Pb$_y$I$_z$) 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.

翻译：理解以原子为中心的配位八面体的空间排列对于关联许多材料家族的结构与性能至关重要。传统的逐个案例检查在发现大型数据集中的趋势和相似性时成为一项艰巨任务。在此，我们通过将化学直觉操作化，利用无监督机器学习实现了配位八面体网络的几何解析、量化和分类的自动化。我们应用该工作流程分析两个数据集以证明其有效性。对于计算生成的单氧化物钙钛矿（ABO$_{3}$）多晶型物，我们揭示了轴依赖性倾斜趋势，这有助于检测氧化态变化。对于来自实测结构的杂化碘铅酸盐（A$_x$Pb$_y$I$_z$），我们对其八面体网络进行了分类，揭示了配位环境的类鲍林连接规则以及支撑其结构多样性的设计原理。我们的结果为材料化学中原子八面体网络的广阔设计空间提供了初步认识，并为特定结构类型的高通量、靶向筛选提供了信息。