MS data addition improves the prediction of specific functional groups. Our MLP model trained on IR data performs well on alkanes, ketones, arenes, carboxylic acids, and esters (average validation F1-score of 0.926) but it did not perform at par to predict nitriles, amines, amides, and acyl halides with an average validation F1-score of 0.663 (Figure , Table ). We included the chemical features captured by mass spectrometry (MS) to augment the MLP-IR model (Figure ) to address these problematic functional groups. First, we trained an MLP model only on MS data to investigate its predictive capacity for functional groups (Figure , Table ). The difference between the F1 scores of the training set compared to the validation set indicates that MS data needs other models to generalize for consistent performance compared to IR data using MLP (Tables ). Similar to the MLP-IR model, the MLP-MS model performed well with more data for a given functional group (e.g. alkanes, arenes, alkyl halides), and poorly when fewer data were available (e.g. acyl halides, amides, and amines). Next, we investigated if combining IR and MS data could improve de novo prediction of functional by concatenating spectral data features into an IR+MS model (see experimental section). Table shows the training and validation F1scores and Table show the 5-fold result of the MLP model. The improvement of the IR+MS model over the IR model is presented as Figure , and the direct F1 scores are shown in Figure with an average improvement of 0.024 overall functional groups. However, combining IR and MS data results in a substantial increase in validation F1 scores for the nitrile, alkene, and alkyl halide functional groups with improvements of 0.124, 0.048, and 0.061 respectively. The amide functional group remains unchanged as the F1 score of 0.563 is the as the MLP-IR model.