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Regensburg 2019 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 61: Semiconductor Surfaces: Adsorption and Reactivity

O 61.4: Vortrag

Mittwoch, 3. April 2019, 15:45–16:00, H25

Analysis of the Catalytic Activation of CO2 by Artificial Intelligence Studies and Extensive DFT Calculations — •Aliaksei Mazheika1, Yanggang Wang1,2, Luca Ghiringhelli1, Sergey V. Levchenko3,1,4, and Matthias Scheffler11Fritz-Haber-Institut der MPG, Berlin, DE — 2University of Shenzhen, Shenzhen, CN — 3Skolkovo Innovation Center, Moscow, RU — 4NUST MISIS, Moscow, RU

Using artificial intelligence (AI) trained on ab initio data, we develop a strategy for the rational design of catalytic materials for converting CO2 to fuels and other useful chemicals. Specifically, we employ the subgroup discovery [1] and sure independence screening and sparsifying operator (SISSO) [2]. For oxide surfaces the results reveal that an electron transfer to the π* antibonding orbital of the adsorbed molecule and the associated bending of the initially linear O-C-O, previously proposed as indicator of activation [3], are insufficient to account for the good catalytic performance. Instead, our AI model identifies the common feature of a group of experimentally studied oxide catalysts in the binding of one molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of the molecular C-O bond. This suggests to use the C-O bond elongation as an indicator of CO2 activation. Based on these findings, we propose a set of new promising catalysts for CO2 conversion, and a recipe to find more.

[1] M. Boley et al., Data Min. Knowl. Disc. 31, 1391 (2017).

[2] R. Ouyang et al., Phys. Rev. M 2, 083802 (2018).

[3] H.-J. Freund and M. W. Roberts, Surf. Sci. Rep. 25, 225 (1996).

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