All ML models used in this work are graph neural networks (GNNs) that are publicly available through the Open Catalyst Project (OCP) GitHub repository. Before training these models, several preprocessing steps were carried out. The data presented in tmQM (ASE Atoms objects and, separately, associated properties) were converted into molecular graphs and stored as LMDBs (Lightning Memory-Mapped Databases) using the AtomstoGraphs functionality in OCP. Since the models available in OCP do not incorporate total charge information, and tmQM includes structures with either a -1, 0, or +1 total charge, a subset of tmQM was created containing only neutral structures. This procedure yielded two versions of tmQM: one containing the entirety of tmQM (containing 86,665 structures) and a neutral subset containing 71,173 structures.