Computational Methodology: The ANI-1x data set was developed using an active learning procedure to enhance the diversity and accuracy of the ANI-1x potential. Initially, an ensemble of ANI models was trained using bootstrapping on a preliminary data set. Molecules were then randomly sampled from extensive databases like GDB-11 and ChEMBL. Four sampling techniques were applied to these molecules within active learning: MD sampling, normal mode sampling, dimer sampling, and torsion sampling. Data points with high uncertainty, identified using the ensemble disagreement measure ρ, were selected for DFT calculations. These high-uncertainty points were then added to the training set, and the models were retrained. This process was iteratively repeated to create a more diverse and comprehensive data set.