Speaker
Description
The FUNCAT center and the C2C collaboration establish a link between the Max Planck Society (Max Planck Institute for Chemical Energy Conversion, Fritz Haber Institute and Max-Planck-Institut für Kohlenforschung) and the Cardiff Catalysis Institute in the UK, which enables the sharing resources and expertise in order to tackle major challenges in heterogeneous catalysis. In particular, CO2 hydrogenation is one of the focuses of this project [1, 2]. Because the formation of valuable products from CO2 relies on a complex interplay of several processes, such as the multiple surface reactions and the dynamic restructuring of the material under reaction conditions, a rational approach to design efficient materials has not been established so far. Herein, we combine leading-edge techniques of the involved centers to develop artificial intelligence (AI) approaches able to blend consistent “clean” data from experiments and first-principles calculations [3]. The goal is to unveil the intricate correlations between key physicochemical descriptive parameters (“materials genes”) and the catalytic performance in the CO2-conversion processes [4]. Crucially, the inclusion of theoretical descriptive parameters in the AI analysis will help identify and understand the microscopic processes that govern catalysis, while circumventing the atomistic description of the full catalytic progression.
References
[1] P. Hergersberg, Treibstoff aus dem Stahlwerk, MaxPlanckForschung Heft 3/2021, 64 (2021). https://www.mpg.de/17671086/MPF_2021_3.pdf
[2] H. Bahruji, J. R. Esquius, M. Bowker, G. Hutchings, R. D. Armstrong, W. Jones, Solvent Free Synthesis of PdZn/TiO2 Catalysts for the Hydrogenation of CO2 to Methanol, Top Catal 61. 144 (2018). https://doi.org/10.1007/s11244-018-0885-6
[3] 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
[4] L. Foppa, et al. and M. Scheffler, Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence, MRS Bulletin 46, 1016-1026 (2021). https://doi.org/10.1557/s43577-021-00165-6
Addresses
(a) Present address: Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Moscow, Russia
(b) Max-Planck-Institut für Kohlenforschung, Mülheim a. d. Ruhr, Germany
(c) Cardiff Catalysis Institute, School of Chemistry, Cardiff University, Cardiff, UK
(d) Present address: The FAIRmat Consortium of the NFDI, Humboldt-Universität zu Berlin, Berlin, Germany
| Abstract Number (department-wise) | SG 10 |
|---|---|
| Department | Scheffler Group |
