Understanding the patterns that neural networks learn from chemical spectra

Analysing spectra from experimental characterization of materials is often time consuming, susceptible to distortions in data, requires specific domain knowledge, and may be susceptible to biases in general heuristics under human analysis. Recent work has shown the potential of using machine learning methods to solve this task, and provide automated and unbiased spectral analysis on- the-fly . We present a simple 1D neural network model to classify infrared spectra from small organic molecules, according to their functional groups. Our model is within range of state-of-the-art performance while being significantly less complex than previous works. A smaller network reduces the risk of overfitting and enables exploring what the model has learned about the underlying physics behind the spectra. Using explainability techniques, we show that our model learns the characteristic group frequencies of functional groups, and additionally uses non-intuitive patterns to classify spectra. We argue for the development of smaller yet accurate models to carry out tasks where model transparency is as important as prediction accuracy