A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction

Roman Schmack, Alexandra Friedrich, Evgenii V. Kondratenko, Jörg Polte, Axel Werwatz and Ralph Kraehnert ()
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Roman Schmack: Institut für Chemie
Alexandra Friedrich: FG Ökonometrie und Wirtschaftsstatistik
Evgenii V. Kondratenko: Leibniz Institute for Catalysis (LIKAT Rostock)
Jörg Polte: Institut für Chemie
Axel Werwatz: FG Ökonometrie und Wirtschaftsstatistik
Ralph Kraehnert: Institut für Chemie

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract Decades of catalysis research have created vast amounts of experimental data. Within these data, new insights into property-performance correlations are hidden. However, the incomplete nature and undefined structure of the data has so far prevented comprehensive knowledge extraction. We propose a meta-analysis method that identifies correlations between a catalyst’s physico-chemical properties and its performance in a particular reaction. The method unites literature data with textbook knowledge and statistical tools. Starting from a researcher’s chemical intuition, a hypothesis is formulated and tested against the data for statistical significance. Iterative hypothesis refinement yields simple, robust and interpretable chemical models. The derived insights can guide new fundamental research and the discovery of improved catalysts. We demonstrate and validate the method for the oxidative coupling of methane (OCM). The final model indicates that only well-performing catalysts provide under reaction conditions two independent functionalities, i.e. a thermodynamically stable carbonate and a thermally stable oxide support.

Date: 2019
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DOI: 10.1038/s41467-019-08325-8

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