In addition to training models from scratch, several transfer learning experiments were run for models trained on tmQM wB97MV. Transfer learning is an approach wherein the models utilize a pretrained checkpoint from a model trained on a different dataset, which is often much larger and more diverse than the one of interest. This allows for much less training data to be required in order to reach the same accuracy, or conversely, much higher accuracy given the same amount of training data. We utilize OC20 in our transfer learning approaches, since it contains a very large amount of DFT data. OC20 consists of heterogeneous catalysts and small adsorbates, which is fairly different from the large molecular systems considered here. However, fine-tuning a model trained on OC20 has shown tremendous promise for various datasets, including molecular data. The experiments done in this work used a simple fine-tuning approach, where the ML models' weights were initialized from models pretrained on OC20 3 and the learning rate was decreased by a factor of ten compared to training from scratch. Much more complex transfer learning approaches are available, which could further improve performance, but the method we employed is extremely simple and has demonstrated success in various domains.