Extracting kinetic information in catalysis: an automated tool for the exploration of small data

Abstract

For numerous reactions in catalysis, the lack of (big) data in kinetics is compensated for by the availability of numerous small, scattered datasets as typically found in the literature. To exploit the potential of such peculiar, small data, incorporation of fundamental knowledge into data-driven approaches is essential. In this work, a novel tool was developed to automatically extract kinetically relevant information from small datasets of steady-state kinetic data for heterogeneously catalysed reactions. The developed tool, based on the principles of qualitative trend analysis, was tailored to the needs of catalysis and enriched with chemical knowledge, balancing thereby the limited amount of data and ensuring that meaningful information is extracted. A detailed account of the development steps discloses how the chemical knowledge was incorporated, such that this approach can inspire new tools and applications. As demonstrated for a hydrodeoxygenation case study, such a tool is the first step into automatic construction of kinetic models, which will ultimately lead to a more rational design of novel catalysts.