Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Dataset

Machine learning (ML) methods have shown promise for discovering novel catalysts, but are often restricted to specific chemical domains. Generalizable ML models require large and diverse training datasets, which exist for heterogeneous catalysis but not for homogeneous catalysis. The tmQM dataset, which contains properties of 86,665 transition metal complexes calculated at the TPSSh/def2-SVP level of density functional theory (DFT), provided a promising training dataset for homogeneous catalyst systems. However, we find that ML models trained on tmQM consistently underpredict the energies of a chemically distinct subset of the data. To address this, we present the tmQM_wB97MV dataset, which filters out several structures in tmQM found to be missing hydrogens and recomputes the energies of all other structures at the wB97M-V/def2-SVPD level of DFT. ML models trained on tmQM_wB97MV show no pattern of consistently incorrect predictions and much lower errors than those trained on tmQM. The ML models tested on tmQM_wB97MV were, from best to worst, GemNet-T > PaiNN ~ SpinConv > SchNet. Performance consistently improves when using only neutral structures instead of the entire dataset. However, while models saturate with only neutral structures, more data continues to improve the models when including charged species, indicating the importance of accurately capturing a range of oxidation states in future data generation and model development. Furthermore, a fine-tuning approach where weights were initialized from models trained on OC20 led to drastic improvements in model performance, indicating transferability between ML strategies of heterogeneous and homogeneous systems.