The tmQM dataset was designed to provide training and evaluation data for machine learning models that could predict properties of transition metal complexes. 11 tmQM consists of 86,665 transition metal complexes taken from the Cambridge Structural Database (CSD), whose geometries were optimized using xTB and given as files listing the atoms in each structure and their cartesian coordinates. tmQM also contains several properties calculated at the TPSSh/def2-SVP level of theory. We focus on the total electronic energy of the complexes since, if one can predict the changes in energy associated with a reaction step, they can use that to determine whether that step is favorable or not, and thus if the structure is a good catalyst.