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  Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

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.

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 Creators:
Foppa, Lucas1, 2, Author           
Ghiringhelli, Luca M.1, 2, Author           
Girgsdies, Frank3, Author           
Hashagen, Maike3, Author           
Kube, Pierre3, Author           
Hävecker, Michael4, Author
Carey, Spencer3, Author           
Tarasov, Andrey3, Author           
Kraus, Peter3, Author           
Rosowski, Frank5, Author
Schlögl, Robert3, 4, Author           
Trunschke, Annette3, Author           
Scheffler, Matthias1, 2, Author           
Affiliations:
1NOMAD, Fritz Haber Institute, Max Planck Society, ou_3253022              
2Humboldt-Universität zu Berlin, Zum Großen Windkanal 6, D-12489 Berlin, Germany, ou_persistent22              
3Inorganic Chemistry, Fritz Haber Institute, Max Planck Society, ou_24023              
4Max Planck Institute for Chemical Energy Conversion, Max Planck Society, Mülheim an der Ruhr, DE, ou_3023867              
5BASF SE, Process Reseach and Chemical Engineering, Heterogeneous Catalysis, Carl-Bosch-Straße 38, D-67065 Ludwigshafen, Germany, ou_persistent22              

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Free keywords: Condensed Matter, Materials Science, cond-mat.mtrl-sci
 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.

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Language(s): eng - English
 Dates: 2021-02-162021-07-212021-10-012021-11
 Publication Status: Published in print
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1557/s43577-021-00165-6
arXiv: 2102.08269
 Degree: -

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Project name : NOMAD CoE - Novel materials for urgent energy, environmental and societal challenges
Grant ID : 951786
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: MRS Bulletin
  Abbreviation : MRS Bull.
Source Genre: Journal
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Publ. Info: Warrendale, PA, USA : Materials Research Society
Pages: 11 Volume / Issue: 46 (11) Sequence Number: - Start / End Page: 1016 - 1026 Identifier: ISSN: 0883-7694
CoNE: https://pure.mpg.de/cone/journals/resource/954925549328