Abstract
Zeolite-confined metal is an important class of heterogeneous catalysts, demonstrating exceptional catalytic performance in many reactions, but the identification of a stable metal-zeolite combination with a simple synthetic method remains a top challenge. Here artificial intelligence methods, particularly global neural network potential based large-scale atomic simulation, are utilized to design Pt-containing zeolite frameworks for propane-to-propene conversion. We show that out of the zeolite database (>220 structure framework) and more than 100,000 Pt/Ge differently distributed configurations, there are only three Ge-containing zeolites, germanosilicate (MFI, IWW and SAO) that are predicted to be capable of stabilizing Pt single atom embedded in zeolite skeleton and at the meantime allowing propane fast diffusion. Among, the Pt(1)@Ge-MFI catalyst is successfully synthesized via a simple one-pot synthesis without a lengthy post-treatment procedure, and characterized by high-resolution experimental techniques. We demonstrate that the catalyst features an in-situ formed [GePtO(3)H(2)] active site under the reductive reaction condition that can achieve long-term (>750 h) high activity and selectivity (98%) for propane dehydrogenation. Our simple catalyst synthesis holds promise for scale-up industrial applications that can now be rooted in first principles via data-driven catalyst design.