Additional functional groups classification does not affect the model performance of the original definitions. In the previous section, we show that some functional groups explicitly trained in the MLP model were incorrectly classified due to overlapping peaks belonging to functional groups that were not included in our original set of functional group types. We hypothesized that the separate classification of the "overlapping" functional groups could affect the performance of our model. To test this hypothesis, we introduced the 'nitro,' 'ether,' and 'aldehyde' groups to the model. The 'nitro' group has significant overlap with the nitrile group (see the previous section), while the 'ether' group did not have peak values which overlapped with other functional groups in our previous definition. Another limitation of our model is the inability to distinguish methyl groups from other alkane functional groups. We propose that this is possible due to the lack of a C-C stretch in methyl groups and methyl groups contain characteristic peaks not present in other alkane groups (i.e. the CH3 bend). In the NIST dataset many alkyl halides are present which do not contain any C-H bonds as all hydrogens in the molecule have been halogenated. Due to the large size of the alkane functional group in the training set, we hypothesize that splitting the alkane group into methyl and 'other' alkanes will not result in a large decrease in performance. Therefore, we decided to subdivide the 'alkane' group into 'methyl,' and 'other' alkanes as these groups performed the best out of all other groups in the original model. show the results of these two hypotheses with details presented in Figure and Tables S9, S10. The relatively high F1 scores for the 'methyl' (0.932) and 'other' alkane (0.936) groups support our hypothesis that sub-division of the original alkane definition does not decrease performance. Figures 5a-b also suggest that our hypothesis to improve low performance of functional groups by the introduction of new functional groups for both IR and IR+MS MLP model is incorrect (compare Tables S9, S10 with Tables ). Although the nitrile and amide groups do not show improvement after the introduction of the nitro and ether groups as the F1 score for nitriles decreased by 0.019 and increased by 0.032, the new groups perform well as compared to the original problematic groups (0.932 for nitro groups and 0.923 for ethers). This suggests that the addition of new functional groups does not cause a significant loss in F1 score for other groups. Therefore, we speculate that more complex groups could be added to the model to provide detailed structural information, such as a model to identify heterocyclic aromatic rings from rings comprised of only carbon. While further subdivision of functional groups is beyond the scope of this work, they present a potential extension of this work towards realization of autonomous instrumentation that results in minimal manual intervention.