Speaker
Description
The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters (“materials genes”) reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of “clean data” [1], containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression based SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design.
A poster discussing the experimental aspects of this work is shown in the AC Department.
References
[1] A. Trunschke, et al. and S. Wrabetz, Towards Experimental Handbooks in Catalysis, Top. Catal. 63, 1683 (2020). https://doi.org/10.1007/s11244-020-01380-2
[2] L. Foppa, et al. and M. Scheffler, Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence, MRS Bull. 46, 1016 (2021). https://doi.org/10.1557/s43577-021-00165-6
Addresses
(a) Present address: The FAIRmat Consortium of the NFDI, Humboldt-Universität zu Berlin, Berlin, Germany
(b) Inorganic Chemistry Department, Fritz Haber Institute, Berlin, Germany
(c) Max Planck Institute for Chemical Energy Conversion, Mülheim a. d. Ruhr, Germany
(d) BasCat BASF UniCat JointLab der Technischen Universität zu Berlin, Berlin, Germany
| Abstract Number (department-wise) | SG 08 |
|---|---|
| Department | Scheffler Group |
