## 3 Results  
The performance of the VOC-Net classifier was evaluated by consideration of confusion matrices and ROC curves. The confusion matrix and ROC curves for performance against the training spectra are shown in Figures 7 and 8. There are eleven misclassifications for the 1377 training spectra, resulting in a classification accuracy for the training dataset of 99.2%. By minimizing the sparse categorical cross-entropy loss, we have limited training to some extent, which reduces the accuracy against the training dataset. However, this approach results in better generalization for the CNN model and more robust performance in validation and in application against experimental spectra, as illustrated in Figure 6b, where the accuracy on validation data is greater than accuracy on training data, indicating that the generalization error is small compared to the training error. The misclassifications for the training spectra are for carbonyl sulfide, hydrogen cyanide, and ethanol spectra. Interestingly, the ethanol spectra is misclassified as carbonyl sulfide and formic acid. As a consequence, in the ROC curve shown in Figure 8, the ethanol and carbonyl sulfide have reduced area-under-curve (AUC) scores. For the classification of simulated validation spectra, the VOC-Net model produces two misclassifications, resulting in a classification accuracy of 99.7% for the 591 validation spectra. The confusion matrix and ROC curves for the validation spectra are shown in Figures 9 and 10. The confusion matrix illustrates that two ethanol spectra were misclassified out of the 591 validation spectra. These misclassifications cause the reduction in the true positive rate of ethanol shown in the ROC curve in Figure 10.  
Figure 6: (**a**) Sparse categorical crossentropy loss and (**b**) accuracy for simulated training and validation spectra. Figure 8: ROC curve for the classification of 1377 simulated training spectra. Figure 7: Confusion matrix for classification of 1377 simulated training spectra. Red boxes indicate misclassifications. In Figure 11, we observe twelve spectra sampled from the validation dataset. One of the two misclassficiations is illustrated in Figure 11, a misclassification for an ethanol spectra at 4.4 Torr. The rest of the sampled Figure 11 spectra are correctly classified. Based on the softmax scores, the model is performing very well, with softmax scores for individual classifications of approximately one. The softmax scores represent the probability that a  
Figure 10: ROC curve for the classification of 591 simulated validation spectra. Figure 9: Confusion matrix for the classification of 591 simulated validation spectra. Red boxes indicate misclassifications. spectra belongs to one of the considered molecules; hence, the softmax scores near unity indicate that the model has a high degree of confidence in these classifications. The misclassified ethanol spectra shown in Figure 11 (top left) is very complex. Based on the softmax scores for this case, it is evident that the model is confused by this complexity and is uncertain as to whether the spectra belongs to methanol, carbonyl sulfide, ethanol, or formic acid. All four of these molecules have complex spectra with hundreds of significant transitions in the present spectral region. Of course, the number of epochs can be increased, additional filters can be added, and/or additional convolutional blocks can be added to increase the classification accuracy and avoid model confusion for these complex spectra, but that runs the risk of building a model that overfits the spectral dataset and will not perform well for the classification of noisy experimental spectra. The spectra for methanol in the third row and the fifth column is similarly complex but it is correctly classified but with the model assigning a small probability that this spectra could belong to acetaldehyde. Thus, classification decisions with softmax scores, as illustrated, offer insight into the certainty of model predictions and encourages acceptance of classification models like VOC-Net.