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Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

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 fu...

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Autores principales: Foppa, Lucas, Ghiringhelli, Luca M., Girgsdies, Frank, Hashagen, Maike, Kube, Pierre, Hävecker, Michael, Carey, Spencer J., Tarasov, Andrey, Kraus, Peter, Rosowski, Frank, Schlögl, Robert, Trunschke, Annette, Scheffler, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825435/
https://www.ncbi.nlm.nih.gov/pubmed/35221466
http://dx.doi.org/10.1557/s43577-021-00165-6
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author Foppa, Lucas
Ghiringhelli, Luca M.
Girgsdies, Frank
Hashagen, Maike
Kube, Pierre
Hävecker, Michael
Carey, Spencer J.
Tarasov, Andrey
Kraus, Peter
Rosowski, Frank
Schlögl, Robert
Trunschke, Annette
Scheffler, Matthias
author_facet Foppa, Lucas
Ghiringhelli, Luca M.
Girgsdies, Frank
Hashagen, Maike
Kube, Pierre
Hävecker, Michael
Carey, Spencer J.
Tarasov, Andrey
Kraus, Peter
Rosowski, Frank
Schlögl, Robert
Trunschke, Annette
Scheffler, Matthias
author_sort Foppa, Lucas
collection PubMed
description 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. IMPACT STATEMENT: Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1557/s43577-021-00165-6.
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spelling pubmed-88254352022-02-23 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 J. Tarasov, Andrey Kraus, Peter Rosowski, Frank Schlögl, Robert Trunschke, Annette Scheffler, Matthias MRS Bull Impact Article 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. IMPACT STATEMENT: Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1557/s43577-021-00165-6. Springer International Publishing 2021-10-01 2021 /pmc/articles/PMC8825435/ /pubmed/35221466 http://dx.doi.org/10.1557/s43577-021-00165-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Impact Article
Foppa, Lucas
Ghiringhelli, Luca M.
Girgsdies, Frank
Hashagen, Maike
Kube, Pierre
Hävecker, Michael
Carey, Spencer J.
Tarasov, Andrey
Kraus, Peter
Rosowski, Frank
Schlögl, Robert
Trunschke, Annette
Scheffler, Matthias
Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
title Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
title_full Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
title_fullStr Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
title_full_unstemmed Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
title_short Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
title_sort materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
topic Impact Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825435/
https://www.ncbi.nlm.nih.gov/pubmed/35221466
http://dx.doi.org/10.1557/s43577-021-00165-6
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