Lewis R., et al. developed an algorithm in 2024 to "quantify the similarity between experimental and calculated IR spectra" using support vector classification. Their method combines IR Spectroscopy and 1H-NMR results, measuring performance as the structure classification characteristic and outperforming either technique alone. Their result saw an improved correct classification rate using 100 comparisons to 87 percent versus approximately 80 percent versus using individual methods . Devata, S., et al. developed DeepSPInN, which is a deep reinforcement learning method that predicts the molecular structure when given infrared and 13C nuclear magnetic resonance spectra. Their molecular structures were formulated using a Markov decision process and employed Monte-Carlo tree search to explore and choose the appropriate actions. Using the QM9 dataset by OpenDataLab, DeepSPInN was able to predict the correct molecular structures for 91.5 percent of the input spectra within an average experimental time of 77 seconds .