Quantifying the Limits of Methane Activation in Cu-exchanged Zeolites using Reactive and Interpretable Machine Learning based Potentials

Natural gas remains an essential energy source for the industrial and residential sectors. However, selective valorization of methane (the main component of natural gas) into more mobile liquid energy carriers such as methanol remains challenging. Inspired by pMMO enzymes, many recent studies have examined Cu-exchanged zeolites as promising catalysts, specifically through [CuOCu]2+ sites. These efforts, in part, have been motivated by the possibility of finding an elusive “Goldilocks” active site or topology that can outperform known catalysts while also maintaining selectivity towards methanol. As large-scale experiments with 1000s of material variations are impossible, theory will likely play an important role. Although computational screening studies are now routine for metals and alloys, similar studies for zeolites are not as straightforward due to the diversity of local chemical environments, and the aforementioned studies are not trivial using the traditional density functional theory (DFT)-based approach. Therefore, the overarching goal of this study is to leverage large-scale DFT calculations to develop a reactive machine learning-based potential (rMLP) capable of systematically sampling the stability and reactivity of all [CuOCu]2+ sites within a representative set of zeolites. Specifically, using methane activation as a prototypical example of an industrially relevant zeolite-catalyzed reaction, we have developed a novel multistage active learning algorithm that preferentially samples the potential energy surface of the system near the transition state of methane activation. We show that the resulting rMLP replaces the expensive DFT-based NEB calculations without any appreciable loss in accuracy (within 0.07 eV of the DFT computed energy barriers) – we evaluate C-H bond activation energies for 5,400 distinct sites across 52 zeolites and obtain 3,356 valid sites suitable for methane activation. By replacing the expensive DFT-based NEB calculations with rMLPs, we now report an exhaustive high-throughput screening study of thousands of [CuOCu]2+ sites in zeolites, comparing the maximum rates of methane activation across 52 zeolite topologies and more than 3,000 sites. To the best of our knowledge, this work represents the first example of using reactive MLPs to identify the transition state geometries and screen the catalytic performance of thousands of zeolite-based active sites at DFT accuracies.