aceRethinking 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…
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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- Peer-reviewedFaraday Discussions2026-09-17
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Truvace Impact Record TRV-2026-1129, v2: “Rethinking catalysis: interpretable AI and description of real-world conditions <i>via</i> materials genes.” Truvace, 2026-11-04. /record/TRV-2026-1129 (accessed on the day you copy it). sha256 1d4265f5ebf2e832…
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Editorial pass: sharper headline; filed as context: no real AI gain or problem (gain activity_only, problem absent).
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