Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
ORAL
Abstract
Heterogeneous catalysis is an example of a complex materials function governed by an intricate interplay of several processes. While modeling the full catalytic progression via first-principles statistical mechanics is impractical, we show how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to determine the key descriptive parameters or materials genes reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of clean data,[1] containing 12 selective-oxidation catalysts which were synthesized, fully characterized, and tested according to standardized protocols. Then, we apply the symbolic-regression SISSO approach[2] to identify the few most relevant materials properties that correlate, in a possibly nonlinear way, with the reactivity.[3]
*L.F. acknowledges the funding from the Swiss National Science Foundation, postdoc mobility Grant P2EZP2_181617 and the NOMAD CoE (European Union's Horizon 2020 research and innovation program under the Grant Agreement No. 951786).
–
Publication: [1] A. Trunschke, et al., Top. Catal. 63, 1683-1699 (2020).
[2] R. Ouyang, et al., Phys. Rev. Mater. 2, 083802 (2018).
[3] L. Foppa, et al., MRS Bull. 46, 1 (2021).
Presenters
-
Matthias Scheffler
- NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society
- Fritz-Haber Institute
- The NOMAD Laboratory at the Fritz Haber Institute of the MPG