We present a theoretical analysis of trends in overpotentials for electrocatalytic CO2 reduction based on density functional theory calculations. The analysis is based on understanding variations in the free energy of intermediates and mapping out the potential at which different elementary steps are exergonic as a measure of the catalytic activity. We study different surface structures and introduce a simple model for including the effect of adsorbate-adsorbate interactions. We find that high coverages of CO under typical reaction conditions for the more reactive transition metals affect the catalytic activity towards the CO2 reduction reaction, but the ordering of metal activities is not changed. For the hydrogen evolution reaction, a high CO coverage shifts the maximum activity towards more reactive metals than Pt.

Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters-reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem makes it very difficult and expensive to solve completely, and even a full solution would not give much insight into the rational design of new catalysts. The descriptor-based approach seeks to determine a few ''descriptors'' upon which the other parameters are dependent. By doing this it is possible to reduce the dimensionality of the problem-preferably to 1 or 2 descriptors-thus greatly reducing computational efforts and simultaneously increasing the understanding of trends in catalysis. The ''CatMAP'' Python module seeks to standardize and automate many of the mathematical routines necessary to move from ''descriptor space'' to reaction rates for heterogeneous (electro) catalysts. The module is designed to be both flexible and powerful, and is available for free online. A ''reaction model'' can be fully defined by a configuration file, thus no new programming is necessary to change the complexity or assumptions of a model. Furthermore, various steps in the process of moving from descriptors to reaction rates have been abstracted into separate Python classes, making it easy to change the methods used or add new functionality. This work discusses the structure of the code and presents the underlying algorithms and mathematical expressions both generally and via an example for the CO oxidation reaction. Graphical Abstract

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