Machine learning could solve the limitations of the more traditional methods to calculate and analyze spectroscopic data as it offers to learn descriptors directly from the available data. The versatility and potential of ML in advancing chemical research was demonstrated by many examples in the past, e.g. by the application of machine learning for molecular property prediction and molecular design. , , , , , One of the earliest applications of artificial neural networks to spectral analysis was to find functional groups from IR spectra, where the authors used a 2-layer feed-forward neural network . The field of machine learning has come a long way since then, with new optimization algorithms and model architectures, delivering state-of-the-art performance. Other attempts implemented similarity search algorithms that harnessed machine learning techniques to extract feature vectors for comparison . Wang et al. used support vector machines to improve the prediction accuracy on a database exported from the OMNIC software [17]. Enders et al. published a method which uses one convolutional neural network per functional group type to find functional groups present in the IR spectra. Their work uses spectra images as input, and therefore can potentially suffer from problems such as activation due to non-spectra pixels in the image and loss of information due to max pooling. Fine et al. combined mass spectra data and IR data at the input of their models. Their model consists of a combination of an auto-encoder and a densely connected neural network.