Classification of Samples via Neural-Network Augmented Two-Dimensional Infrared Spectroscopy

The application of artificial neural network (ANN) techniques to spectroscopy has proven to be a powerful tool for the rapid and accurate classification of experimental samples. However, despite the unique abilities of two-dimensional infrared spectroscopy (2D IR), the use of ANNs to classify samples on the basis of their 2D-IR spectra has been unexplored. We present two investigations into utilizing ANNs to perform end-to-end classification of samples from their 2D-IR spectra. In the first, we construct a model that can perform a binary classification of experimental samples on the basis of their solvent. In the second, we demonstrate that classification is possible even for a single spectral slice of pump-delay and waiting-time combination even when samples display almost identical spectra. These results clearly demonstrate the potential of ANN-augmented 2D IR, with particular emphasis on its use as a technology for high-throughput screening applications.