The best available catalysts for the reduction of carbonyl groups in ketones are
currently based on rare metals such as platinum, osmium or ruthenium. The developmentof alternative catalysts made from more abundant metal such as manganese offers chances to increase sustainability and cost efficiency. Analysing and predicting chemical and physical properties with a computational would streamline the development process by allowing early elimination of options and by proposing new structures with a high chance of success. Density-functional theory (DFT) can predict structures and ligand properties at a high level. In this investigation, around 40 ligands were defined based on previously researched manganese compounds with known catalytic properties or previously investigated ruthenium complexes. Properties such as hydricity, CO frequencies and bond lengths were calculated and linked to known catalytic activity from the literature.No hard correlations between the investigated properties and reported catalytic activities have been found. Some links however do appear to exist such as a connection between high calculated hydricities and a higher reported catalytic activity.