In this tutorial we highlight the optimal working methodology for discovering novel heterogeneous catalysts using modern tools. First, we give a structure to the discovery and optimisation process, explaining its iterative nature. 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. 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. 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. 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.
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