Rethinking catalysis: interpretable AI and description of real-world conditions via materials genes

Descriptors link basic physicochemical parameters that characterize the materials and the environment to the catalytic performance. Traditionally, descriptors are rooted in mechanistic understanding of elementary surface reactions gained from surface science and atomistic simulations on well-defined surfaces and under vacuum. However, real-world catalysis operates under elevated pressures and temperatures, where an intricate interplay of multiple physical processes, including significant materials' restructuring and transport phenomena, governs performance. To bridge this gap, we introduced an interpretable artificial intelligence (AI) approach that identifies key physicochemical parameters correlated with the measured catalytic performance. Analogous to genes in biology and medicine, these "materials genes" provide a statistical description of catalysis without requiring the explicit atomistic description of the underlying physical processes. Here, we combine the sure-independence-screening-and-sparsifying-operator (SISSO) symbolic-regression AI approach with a sensitivity analysis based on partial derivatives to determine the most influential genes needed to describe the selectivity of supported palladium-based metal alloy nanoparticles in the hydrogenation of concentrated acetylene streams. The identified genes include the calculated average d-band center and the measured average particle diameter, indicating the crucial role of adsorption and structure sensitivity on the formation of ethylene.