Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community

Nano-sized Metallic Nickel Clusters Stabilized on Dealuminated beta‑Zeolite: A Highly Active and Stable Ethylene Hydrogenation Catalyst

Supported Ni catalysts were synthesized using the beta-zeolite framework, with and without the framework Al, as a platform for dispersing Ni. The silanol nest sites of dealuminated zeolite beta provide isolated cationic Ni sites that can be reduced under relatively mild conditions to create highly dispersed metal clusters. Compared to the Ni sites present in Ni-[Al]-beta-19, Ni-[DeAl]-beta exhibit a 20-fold increase in the apparent reaction rate for C2H4 hydrogenation and is stable, with little deactivation over 16 h of catalysis. Ni K-edge X-ray absorption spectroscopy (XAS), as well as CO adsorption monitored with Fourier transform infrared spectroscopy, shows that in the oxidized Ni-[DeAl]-beta catalyst Ni reoccupies vacant silanol nests produced from dealumination. After reductive treatment, XAS shows that approximately 50% of Ni is reduced to metallic Ni, forming clusters that are approximately 1 nm in size. Scanning transmission electron microscopy images are consistent with the absence of large (>1 nm) metallic Ni clusters. These results indicate that [DeAl]-beta can be used to synthesize isolated cationic Ni sites as well as stabilize highly dispersed metal clusters that can be used as a highly active and stable C2H4 hydrogenation catalyst