Helfrecht Benjamin A, Pireddu Giovanni, Semino Rocio, Auerbach Scott M, Ceriotti Michele
Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland.
PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS 24 rue Lhomond 75005 Paris France.
Digit Discov. 2022 Oct 12;1(6):779-789. doi: 10.1039/d2dd00056c. eCollection 2022 Dec 5.
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.
沸石是纳米多孔铝硅酸盐骨架，广泛用作催化剂和吸附剂。尽管通过计算机辅助搜索可以生成数百万种硅质网络，但尚未合成出任何新的假设框架。在大量预测结构数据库中寻找有前景的候选物这一大海捞针般的问题已经困扰材料科学家数十年；然而，迄今为止，关于沸石问题的大多数工作都局限于直观的结构描述符。在这里，我们通过一种严格的数据科学方案——“沸石分院帽”来解决这个问题，该方案利用原子间相关性来区分真实和假设的沸石，并将真实沸石划分为组成类别，从而指导针对给定假设框架的合成策略。我们发现，无论“沸石分院帽”使用何种结构描述符，仍有一些假设框架被错误地归类为真实框架，这表明它们可能是很好的合成候选物。我们试图通过使用尽可能完整的结构描述符来尽量减少此类错误分类框架的数量，从而专注于真正可行的合成目标，同时发现作为“沸石分院帽”输出结果的区分真实和假设框架的结构特征。可以基于热力学稳定性和/或它们对所需应用的适用性对候选物进行进一步排序。基于此工作流程，我们提出了三种摩尔体积范围不同的假设框架作为合成的首要目标，每种框架都有“沸石分院帽”建议的组成。最后，我们用包括先前研究中报道的直观描述符在内的一系列结构描述符分析了“沸石分院帽”的行为，发现直观描述符产生的错误分类假设框架明显更多，而且更严格的原子间相关性表明3.2 - 3.4 Å左右的第二近邻Si - O距离是关键的区分因素。
