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Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation

[Image: see text] Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called “mater...

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Autores principales: Foppa, Lucas, Rüther, Frederik, Geske, Michael, Koch, Gregor, Girgsdies, Frank, Kube, Pierre, Carey, Spencer J., Hävecker, Michael, Timpe, Olaf, Tarasov, Andrey V., Scheffler, Matthias, Rosowski, Frank, Schlögl, Robert, Trunschke, Annette
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936587/
https://www.ncbi.nlm.nih.gov/pubmed/36745555
http://dx.doi.org/10.1021/jacs.2c11117
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author Foppa, Lucas
Rüther, Frederik
Geske, Michael
Koch, Gregor
Girgsdies, Frank
Kube, Pierre
Carey, Spencer J.
Hävecker, Michael
Timpe, Olaf
Tarasov, Andrey V.
Scheffler, Matthias
Rosowski, Frank
Schlögl, Robert
Trunschke, Annette
author_facet Foppa, Lucas
Rüther, Frederik
Geske, Michael
Koch, Gregor
Girgsdies, Frank
Kube, Pierre
Carey, Spencer J.
Hävecker, Michael
Timpe, Olaf
Tarasov, Andrey V.
Scheffler, Matthias
Rosowski, Frank
Schlögl, Robert
Trunschke, Annette
author_sort Foppa, Lucas
collection PubMed
description [Image: see text] Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called “materials genes” of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property–function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N(2) adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides “rules” on how the catalyst properties may be tuned in order to achieve the desired performance.
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spelling pubmed-99365872023-02-18 Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation Foppa, Lucas Rüther, Frederik Geske, Michael Koch, Gregor Girgsdies, Frank Kube, Pierre Carey, Spencer J. Hävecker, Michael Timpe, Olaf Tarasov, Andrey V. Scheffler, Matthias Rosowski, Frank Schlögl, Robert Trunschke, Annette J Am Chem Soc [Image: see text] Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called “materials genes” of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property–function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N(2) adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides “rules” on how the catalyst properties may be tuned in order to achieve the desired performance. American Chemical Society 2023-02-06 /pmc/articles/PMC9936587/ /pubmed/36745555 http://dx.doi.org/10.1021/jacs.2c11117 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Foppa, Lucas
Rüther, Frederik
Geske, Michael
Koch, Gregor
Girgsdies, Frank
Kube, Pierre
Carey, Spencer J.
Hävecker, Michael
Timpe, Olaf
Tarasov, Andrey V.
Scheffler, Matthias
Rosowski, Frank
Schlögl, Robert
Trunschke, Annette
Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation
title Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation
title_full Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation
title_fullStr Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation
title_full_unstemmed Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation
title_short Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation
title_sort data-centric heterogeneous catalysis: identifying rules and materials genes of alkane selective oxidation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936587/
https://www.ncbi.nlm.nih.gov/pubmed/36745555
http://dx.doi.org/10.1021/jacs.2c11117
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