SKM 2023 – wissenschaftliches Programm
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O: Fachverband Oberflächenphysik
O 78: Heterogeneous Catalysis and Surface Dynamics II
O 78.7: Vortrag
Donnerstag, 30. März 2023, 12:00–12:15, TRE Phy
Identifying materials genes describing selectivity of catalytic CO2 hydrogenation: an AI approach with theoretical and experimental data — •Ray Miyazaki1, Kendra Belthle2, Harun Tüysüz2, Lucas Foppa1, and Matthias Scheffler1 — 1The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Germany — 2Max-Planck-Institut für Kohlenforschung, Germany
Investigating CO2 hydrogenation by heterogeneous catalysis that mimics hydrothermal vents leads to a deeper understanding of the origin of organic molecules at the early earth [1]. We focus on cobalt nanoparticles supported on M-SiO2 where hetero atoms (e.g., Ti or Al) are incorporated into SiO2. However, heterogeneous catalysis is governed by an intricate interplay among multi-scale processes. Thus, it is rather difficult, if not impossible, to identify the key physical parameters correlated with the catalytic performance (materials genes) directly by theoretical or experimental approaches. In this study, materials properties obtained from density functional theory calculations and experiments, such as adsorption energy of CO2 and measured pore volume, are used to model the experimental selectivity of each organic molecule (e.g., CH3OH, CH4) by the sure-independence screening and sparsifying operator (SISSO) AI approach [2]. In order to accelerate catalyst design, we also investigate the accuracy of the models using input parameter sets with the different acquisition cost.
[1] K. S. Belthle et al., J. Am. Chem. Soc., in press (2022).
[2] R. Ouyang et al., Phys. Rev. Mater., 2, 083802 (2018).