Our split network architecture is based on the idea of learning two separate representations of distinct parts of the IR spectra, the noisy part and the cleaner part of the spectra. These regions are usually known as the 'fingerprint' region and the 'functional group' region, respectively. Therefore, the model has two separate input processing units which both have multiple densely connected layers. The features learned by these units are then concatenated and further processed by a single joint densely connected neural network to generate the functional group detection output (see Figure ). The model that is splitting spectra at 1800 cm -1 wavenumber showed the best performance. Therefore we select this model for further experiments. Further details about hyperparameter search can be found in the supplementary information section. Table shows the optimal network architectures chosen from hyperparameter search.