![]() ![]() 8,9 Concurrently, the field of biomass conversion is maturing. 5–7 Where many of these molecules are now extracted from oil or its subsequent cracking product in the refining process, novel processes are needed considering light alkanes like methane, ethane and propane as the feeds. This requires novel catalytic pathways to, for example, C 2–C 4 olefins 1–4 and aromatics. With the now commonly acknowledged abundance of fossil resources like shale gas, refineries around the world will shift feedstocks from oil to gas. Introduction Research in the discovery of novel heterogeneous catalysts faces an enormous challenge in years to come. Based on the above examples and principles, we then return to the general case, and discuss the application of data-driven workflows in catalyst discovery and optimisation. The basic principles are illustrated with four concrete examples: oxidative methane coupling catalytic hydrogenation of 5-ethoxymethylfurfural optimising bimetallic catalysts in a continuous reactor system, and linking material properties to chemisorption energies for a variety of catalysts. Finally, we explain the importance of experimental and model validation, and show how by combining experimental design, descriptor modelling, and experimental validation you can increase the chances of discovering and optimising good catalysts. Then, we focus in turn on each step of catalyst synthesis, catalysts testing, integrating low-level and high-level descriptor models into the workflow, and explorative data analysis. First, we give a structure to the discovery and optimisation process, explaining its iterative nature. In this tutorial we highlight the optimal working methodology for discovering novel heterogeneous catalysts using modern tools. ![]()
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