Ma Sicong, Liu Zhi-Pan
Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, China.
Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai, 200433, China.
Nat Commun. 2022 May 17;13(1):2716. doi: 10.1038/s41467-022-30522-1.
Heterogeneous catalysts are often composite materials synthesized via several steps of chemical transformation, and thus the atomic structure in composite is a black-box. Herein with machine-learning-based atomic simulation we explore millions of structures for MFI zeolite encapsulated PtSn catalyst, demonstrating that the machine-learning enhanced large-scale potential energy surface scan offers a unique route to connect the thermodynamics and kinetics within catalysts' preparation procedure. The functionalities of the two stages in catalyst preparation are now clarified, namely, the oxidative clustering and the reductive transformation, which form separated SnO and PtSn alloy clusters in MFI. These confined clusters have high thermal stability at the intersection voids of MFI because of the formation of "Mortise-and-tenon Joinery". Among, the PtSn clusters with high Pt:Sn ratios (>1:1) are active for propane dehydrogenation to propene, ∼10 in turnover-of-frequency greater than conventional PtSn metal. Key recipes to optimize zeolite-confined metal catalysts are predicted.
多相催化剂通常是通过几步化学转化合成的复合材料，因此复合材料中的原子结构是一个黑箱。在此，我们利用基于机器学习的原子模拟，探索了数百万种MFI沸石封装的PtSn催化剂结构，表明机器学习增强的大规模势能面扫描为在催化剂制备过程中连接热力学和动力学提供了一条独特的途径。现在阐明了催化剂制备中两个阶段的功能，即氧化团聚和还原转化，它们在MFI中形成了分离的SnO和PtSn合金簇。由于形成了“榫卯连接”，这些受限簇在MFI的交叉空隙处具有高热稳定性。其中，高Pt:Sn比（>1:1）的PtSn簇对丙烷脱氢制丙烯具有活性，周转频率比传统PtSn金属高约10倍。预测了优化沸石限域金属催化剂的关键方法。
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