White-box statistical-learning techniques applied on catalysis
Albert Sabadell-Rendón a b, Sergio Pablo-García a b, Santiago Morandi a b, Núria López a b
a Institute of Chemical Research of Catalonia (ICIQ), Avda. Països Catalans 16, Tarragona, Spain
b Quimica Fisica i Inorganica, Universitat Rovira i virgili, Pl. Imperial Tarraco 1, Tarragona, 43005
Proceedings of International Conference on Frontiers in Electrocatalytic Transformations (INTERECT22)
València, Spain, 2022 November 21st - 22nd
Organizers: Sara Barja, Nongnuch Artrith and Matthew Mayer
Oral, Albert Sabadell-Rendón, presentation 012
DOI: https://doi.org/10.29363/nanoge.interect.2022.012
Publication date: 11th October 2022

Multi-Scale modeling has been the standard to explain and predict activity and selectivity of chemical reactions during the last 20 years[1,2]. This procedure allowed us to predict semiquantitatively the activity trends, recovering the classical volcano plots. The later models lead to successful catalyst optimization using a reduced set of energy descriptors. However, the accuracy of Multi-Scale modeling confronts several limitations with complexity, mainly caused by the coverage effects, the catalyst phase or surface reconstructions, large reaction networks, and highly dynamic materials[1-3]. Statistical Learning (SL) techniques can overcome such limitations. Nevertheless, the black-box nature of most of the SL techniques hinders the physical interpretation of the results. In this work, we present a procedure to generate physical interpretable models able to correlate experimental activity and selectivity with ab-initio Density Functional Theory (DFT)-based descriptors. Here, we applied our methodology on the CH2X2 (X=Cl, Br) hydrodehalogenation reaction family catalyzed by transition metals[3].  Even if this study is based on thermochemical systems, it provides a starting point to solve more complex chemical problems, such as explaining the dynamic charge exchange of single metal atoms on Ceria[4] or electrochemistry.

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