and ANI-ccx trained models degrade when one investigates a broader sampling of chemical space. In Sec.3.2 we train to all four datasets and show that the ANI-1x and ANI-ccx output heads improve in this regard. We also show that predictions at these levels of theory greatly improve for chemical spaces that are lacking in the corresponding datasets: ionic molecules and intermolecular interactions. By multitask learning to datasets that cover these new chemical spaces, knowledge can be transferred amongst the output heads. We offer some concluding remarks in Sec. 5