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）是一类纳米多孔铝硅酸盐骨架材料，被广泛用作催化剂与吸附剂。尽管通过计算机辅助搜索（computer-aided searches）可生成数百万种硅质骨架，但目前尚未成功合成任何新型假想骨架（hypothetical framework）。在海量预测结构数据库中筛选出具备应用潜力的候选骨架，这一“大海捞针”式的难题困扰材料科学家已有数十载；然而迄今为止，针对沸石问题的多数研究仍局限于直观结构描述符（intuitive structural descriptors）的使用。 本研究依托一套严谨的数据科学方案——“沸石分选帽”（zeolite sorting hat）——攻克该难题：该方案利用原子间关联（interatomic correlations）特性，实现真实沸石与假想骨架的区分，并将真实沸石划分为不同组成类别（compositional classes），以此为给定假想骨架指导合成策略制定。 研究发现，无论“沸石分选帽”采用何种结构描述符，仍存在部分假想骨架被错误归类为真实沸石，这提示它们或许是极具合成可行性的候选对象。我们力求通过采用尽可能完备的结构描述符，减少此类分类错误的骨架数量，从而聚焦于真正具备合成价值的目标；同时通过“沸石分选帽”的输出结果，提炼出区分真实与假想沸石的核心结构特征。 此外，还可基于热力学稳定性（thermodynamic stability）及/或目标应用适配性，对候选骨架开展进一步排序。基于该工作流程，我们提出了三类摩尔体积范围各异的假想骨架作为顶级合成候选目标，每类均配有“沸石分选帽”推荐的组成方案。 最后，我们结合此前研究中报道的直观结构描述符，通过不同层级的结构描述符分析了“沸石分选帽”的运行特性，结果表明：直观结构描述符会产生显著更多的分类错误假想骨架；而更严谨的原子间关联分析则指出，第二近邻Si-O键距（second-neighbor Si-O distances）约为3.2–3.4埃（Å）是关键的区分因素。