• generate more QM training data, focusing on poorly described molecules as well as similar instances applicable to other chemistries. For example, to tackle the deficiency in sulfonamides we may need to artificially create a dataset of sulfonamides which have heteroatom neighbors for training and testing purposes. A similar procedure can be applied to other functional groups that are susceptible to strong changes in local chemical environment, such as conjugation, when hetero atom neighbors are present. • determine whether there is a need to split the bond, angle or torsion parameters in question into more specific SMIRKS patters, and whether to split based on either based on geometry features of particular subsets and whether they are being described well, or based on the range of values each of the applied parameters would sample if we performed a custom fit of parameters on each of the subset molecules,