Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the “zeolite sorting hat”—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the zeolite sorting hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the zeolite sorting hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the zeolite sorting hat. Finally, we analyze the behavior of the zeolite sorting hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si-O distances around 3.2–3.4 Å as the key discriminatory factor.

沸石（Zeolites）是一类具有纳米孔道的铝硅酸盐骨架材料，被广泛用作催化剂与吸附剂。尽管借助计算机辅助搜索可生成数百万条硅质骨架网络，但迄今尚未成功合成任何全新的假想沸石骨架。在海量预测结构数据库中筛选出极具潜力的候选骨架，这一如同大海捞针的难题困扰了材料科学家数十年之久；然而迄今为止，针对沸石相关问题的绝大多数研究仍局限于直观的结构描述符（structural descriptor）。本文通过一套严谨的数据科学方案——“沸石分选帽（zeolite sorting hat）”——来解决这一难题，该方案利用原子间关联关系来区分真实沸石与假想沸石，并将真实沸石划分为不同的组成类别，以此为给定的假想沸石骨架指导合成策略。研究发现，无论沸石分选帽采用何种结构描述符，仍存在部分假想沸石骨架被错误归类为真实骨架，这表明这些骨架或许是极具潜力的合成候选对象。我们力求通过采用尽可能全面的结构描述符，尽可能减少这类被错分的骨架数量，从而聚焦于真正具备合成可行性的目标骨架，同时借助沸石分选帽的输出结果，挖掘出能够区分真实与假想沸石骨架的结构特征。还可基于热力学稳定性以及/或其适配目标应用的程度，对候选骨架进行进一步排序。基于此工作流程，我们提出了三款摩尔体积范围各异的假想沸石骨架作为顶级合成目标，每款骨架的组成均由沸石分选帽给出建议。最后，我们采用包含既往研究中报道的直观描述符在内的多层级结构描述符，对沸石分选帽的运行机制进行了分析，结果表明直观描述符会产生显著更多的错分假想沸石骨架，而更严谨的原子间关联关系则显示，第二近邻的硅-氧（Si-O）间距约为3.2–3.4 埃（Å）是关键的区分因素。