Active phase discovery in heterogeneous catalysis via topology-guided sampling and machine learning

Abstract

Understanding active phases across interfaces, interphases, and even within the bulk under varying external conditions and environmental species is critical for advancing heterogeneous catalysis. Describing these phases through computational models faces the challenges in the generation and calculation of a vast array of atomic configurations. Here, we present a framework for the automatic and efficient exploration of active phases. This approach utilizes a topology-based algorithm leveraging persistent homology to systematically sample configurations across diverse coordination environments and material morphologies. Simultaneously, efficient machine learning force fields enable rapid computations. We demonstrate the effectiveness of this framework in two systems: hydrogen absorption in Pd, where hydrogen penetrates subsurface layers and the bulk, inducing a “hex” reconstruction critical for CO2 electroreduction, explored through 50,000 sampled configurations; and the oxidation dynamics of Pt clusters, where oxygen incorporation renders the clusters less active during oxygen reduction reactions, investigated through 100,000 sampled configurations. In both cases, the predicted active phases and their impacts on catalytic mechanisms closely align with previous experimental observations, indicating that the proposed strategy can model complex catalytic systems and discovery active phases under specific environmental conditions. Discovering active phases in heterocatalysis entails efficient configuration sampling and optimization. Here, the authors developed a framework based on topology and machine learning to effectively explore the active structures, applied in the CO2 electroreduction and Oxygen Reduction Reaction

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