SKM 2023 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 78: Heterogeneous Catalysis and Surface Dynamics II

O 78.1: Vortrag

Donnerstag, 30. März 2023, 10:30–10:45, TRE Phy

Data-centric heterogeneous catalysis: identifying rules and materials genes of alkane selective oxidation — •Lucas Foppa1, Frederik Rüther2, Michael Geske2, Gregor Koch3, Frank Girgsdies3, Pierre Kube3, Spencer J. Carey3, Michael Hävecker4, Olaf Timpe3, Andrey Tarasov3, Matthias Scheffler1, Frank Rosowski2, Robert Schlögl3,4, and Annette Trunschke31The NOMAD Lab. at the FHI of the MPG and IRIS-Adlershof of HU Berlin — 2BasCat Lab. at TU Berlin — 3FHI of the MPG — 4Max Planck Institute for Chemical Energy Conversion

Artificial Intelligence (AI) can accelerate materials design by identifying the key parameters correlated with the performance. However, widely used AI methods require big data, and only the smallest part of catalysis-research data meets the quality requirement for data-efficient AI. We use rigorous experimental procedures[1] to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts towards ethane, propane, and n-butane oxidation. By applying the sure-independence-screening-and-sparsifying-operator approach to the so-obtained data set, we identify nonlinear property-function relationships depending on several key parameters and reflecting the intricate interplay of processes governing selective oxidation.[2,3]

[1] A. Trunschke, et al., Top. Catal. 63, 1683-1699 (2020).

[2] L. Foppa, et al., MRS Bull. 46, 1 (2021).

[3] L. Foppa, et al., ChemRxiv, 10.26434/chemrxiv-2022-xmg75 (2022).

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