Of the two model architectures described in Sec. II D, models trained using the second architecture uniformly outperformed those trained using the first architecture for all descriptors. For the second model architecture, Fig. shows the performance of ML models generated from various 2-D (using SMILES strings) and 3-D descriptors (using each of the three optimized geometries -'TIGHTOPT', 'LOOSEOPT', and 'FORCE-FIELD'). The y-axis of the plot corresponds to the sum of the MAEs of the six predictions for each molecule of the test set.