Regensburg 2022 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 25: Data Driven Materials Science: Computational Frameworks / Chemical Complexity
MM 25.4: Vortrag
Mittwoch, 7. September 2022, 16:45–17:00, H46
Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence — •Lucas Foppa1, Christopher Sutton1, Luca M. Ghiringhelli1, Sandip De2, Patricia Löser3, Stephan Schunk2,3, Ansgar Schäfer2, and Matthias Scheffler1 — 1The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, Germany — 2BASF SE, Germany — 3hte GmbH, Germany
The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the very small number of good catalysts. Here, we show how the subgroup-discovery (SGD) artificial-intelligence local approach[1] can be applied to an experimental plus theoretical data set to identify constraints or rules on key physicochemical parameters that exclusively describe materials and reaction conditions with outstanding catalytic performance.[2] By using high-throughput experimentation, 120 SiO2-supported catalysts containing Ru, W and P were synthesized and tested in propylene oxidation. As candidate descriptive parameters, the temperature and ten calculated parameters related to the composition and chemical nature of elements in the catalyst materials, were offered. The temperature, the P content, and the composition-weighted electronegativity are identified as key parameters describing high yields of value-added oxygenate products. The SG rules reflect the underlying processes associated to high performance, and guide catalyst design.
[1] B.R. Goldsmith, et al., New. J. Phys. 19, 013031 (2017).
[2] L. Foppa, et al., ACS Catal. 12, 2223 (2022).