Materials genes of heterogeneous catalysis from clean experiments and 
artificial intelligence

Foppa, Lucas; Ghiringhelli, Luca M.; Girgsdies, Frank; Hashagen, Maike; Kube, Pierre; Hävecker, Michael; Carey, Spencer; Tarasov, Andrey; Kraus, Peter; Rosowski, Frank; Schlögl, Robert; Trunschke, Annette; Scheffler, Matthias

doi:10.1557/s43577-021-00165-6

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Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

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Foppa, Lucas
NOMAD, Fritz Haber Institute, Max Planck Society;
Humboldt-Universität zu Berlin;

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Ghiringhelli, Luca M.
NOMAD, Fritz Haber Institute, Max Planck Society;
Humboldt-Universität zu Berlin;

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Girgsdies, Frank
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

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Hashagen, Maike
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons21768

Kube, Pierre
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

Hävecker, Michael
Max Planck Institute for Chemical Energy Conversion, Max Planck Society;

/persons/resource/persons227633

Carey, Spencer
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons32715

Tarasov, Andrey
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons232541

Kraus, Peter
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons22071

Schlögl, Robert
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;
Max Planck Institute for Chemical Energy Conversion, Max Planck Society;

/persons/resource/persons22181

Trunschke, Annette
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons22064

Scheffler, Matthias
NOMAD, Fritz Haber Institute, Max Planck Society;
Humboldt-Universität zu Berlin;

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2102.08269.pdf
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Foppa2021_Article_MaterialsGenesOfHeterogeneousC.pdf
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Citation

Foppa, L., Ghiringhelli, L. M., Girgsdies, F., Hashagen, M., Kube, P., Hävecker, M., et al. (2021). Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence. MRS Bulletin, 46(11), 1016-1026. doi:10.1557/s43577-021-00165-6.

Cite as: https://hdl.handle.net/21.11116/0000-0008-6F35-1

Abstract

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 here 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,” containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression 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.