An end-to-end framework for reactivity in heterogeneous catalysis

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Morandi, Santiago
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Loveday, Oliver
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Renningholtz, Tim
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Pablo-García, Sergio
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Vargas-Hernández, Rodrigo A.
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Seemakurthi, Ranga Rohit
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Sanz Berman, Pol
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García-Muelas, Rodrigo
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Aspuru-Guzik, Alán
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López, Núria
dc.date.accessioned
2026-03-20T07:48:34Z
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2026-03-20T07:48:34Z
dc.date.issued
2026-03-19
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https://hdl.handle.net/2072/489363
dc.description.abstract
The rationalization of catalytic processes relies on the fundamental understanding of competing reaction mechanisms driving reactants to products. The list of elementary steps composing the reaction networks is proposed based on chemical intuition and evaluated via density functional theory. This approach is limited by the size of the network and disregards alternative paths. Here we present the Catalytic Automated Reaction Evaluator (CARE), a flexible end-to-end framework for heterogeneous catalysis composed of (1) a rule-based reaction network generator, (2) a thermodynamic and kinetic parameter evaluator powered by state-of-the-art machine learning models and (3) a fast microkinetic solver. CARE reproduces the experimental activity trends in methanol decomposition, identifies the selectivity to C3 products in CO2 electroreduction and generates the Fischer–Tropsch synthesis mechanism including 370,000 reactions reaching C6 products. This comprehensive framework enables the exploration of thermal and electrocatalytic reactions previously not amenable to atomistic simulations.
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12 p.
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eng
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Springer Nature
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Attribution-NonCommercial-NoDerivatives 4.0 International
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http://creativecommons.org/licenses/by-nc-nd/4.0/
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RECERCAT (Dipòsit de la Recerca de Catalunya)
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Química
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dc.title
An end-to-end framework for reactivity in heterogeneous catalysis
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info:eu-repo/semantics/article
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54
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info:eu-repo/semantics/publishedVersion
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cap
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S.M., O.L., R.R.S., P.S.B. and N.L. thank the Spanish Ministry of Science and Innovation (PID2024-122516OBI00 and PRE2022-101291)
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NCCR Catalysis (grant number 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation
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O.L. acknowledges the Joan Oró Predoctoral Programme of the Generalitat de Catalunya, and the European Social Fund Plus (2023 FI-1 00769)
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T.R. acknowledges support from the Erasmus+ program of the European Union. S.P.-G. acknowledges support from the US Department of Energy, Office of Science, Subaward by University of Minnesota, with the project title ‘Development of Machine Learning and Molecular Simulation Approaches to Accelerate the Discovery of Porous Materials for Energy-Relevant Applications’ (DE-SC0023454).
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R.R.S. acknowledges funding from European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement number 754510
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A.A.-G. acknowledges support from the Canada 150 Research Chairs Program as well as A. G. Fröseth.
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dc.identifier.doi
https://doi.org/10.1038/s44286-026-00361-8
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dc.rights.accessLevel
info:eu-repo/semantics/openAccess


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