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TRV-2026-1129Certified recordPeer-reviewed

Rethinking catalysis: interpretable AI and description of real-world conditions <i>via</i> 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…

Science · G Space — documented gain · certified 2026-09-18 · v1 · article view · machine-readable

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An interpretable SISSO symbolic-regression AI approach identified materials genes, including d-band center and particle diameter, that statistically describe selectivity of supported palladium-based alloy nanoparticles during hydrogenation of concentrated acetylene streams under elevated pressure and temperature.

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Truvace Impact Record TRV-2026-1129, v1: “Rethinking catalysis: interpretable AI and description of real-world conditions <i>via</i> materials genes.” Truvace, 2026-09-18. /record/TRV-2026-1129 (accessed at citation time). sha256 9dc3f7018d367b36…

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