Infrared spectroscopy is a ubiquitous technique used to characterize unknown materials in the form of solids, liquids, or gases by identifying the constituent functional groups of molecules through the analysis of obtained spectra. The conventional method of spectral interpretation demands the expertise of a trained spectroscopist as it is tedious and prone to error, particularly for complex molecules which have poor representation in the literature. Herein, we present a novel method for automatically identifying functional groups in molecules given the corresponding infrared spectra, which requires no recourse to database-searching, rule-based, or peak-matching methods. Our model employs convolutional neural networks that are capable of successfully classifying thirty-seven functional groups which have been trained and tested on 50,936 infrared spectra and 30,611 unique molecules. Our approach demonstrates its practical relevance in the autonomous analytical identification of functional groups in organic molecules from infrared spectra.

We design and train a neural network (NN) model to efficiently predict the infrared spectra of interstellar polycyclic aromatic hydrocarbons (PAHs) with a computational cost many orders of magnitude lower than what a first-principles calculation would demand. The input to the NN is based on the Morgan fingerprints extracted from the skeletal formulas of the molecules and does not require precise geometrical information such as interatomic distances. The model shows excellent predictive skill for out-of-sample inputs, making it suitable for improving the mixture models currently used for understanding the chemical composition and evolution of the interstellar medium. We also identify the constraints to its applicability caused by the limited diversity of the training data and estimate the prediction errors using a ensemble of NNs trained on subsets of the data. The power of these topological descriptors is demonstrated by the limited effect of including detailed geometrical information in the form of Coulomb matrix eigenvalues.

Researchers at the Universities of Vienna and Göttingen have succeeded in developing a method for predicting molecular infrared spectra based on artificial intelligence. These chemical "fingerprints" could only be simulated by common prediction techniques for small molecules in high quality. With the help of the new technology, which is based on neuronal networks similar to the human brain and is therefore capable of learning, the team led by Philipp Marquetand from the Faculty of Chemistry at the University of Vienna was able to carry out simulations that were previously not possible. The potential of this new strategy has now been published in the current issue of the journal "Chemical Science". Drastic advances in research of artificial intelligence have led to a wide range of fascinating developments in this area over the last decade.