Language Model Enabled Structure Prediction from Infrared Spectra of Mixtures

Transformer language models recently enabled molecular structure prediction directly from infrared (IR) spectra, yet have remained confined to pure compounds. We show that the same architecture learns the correlations embedded in binary mixture spectra and can retrieve the individual molecular components. Trained solely on gas-phase data, our model attains a Top-10 accuracy of 61.4% on balanced synthetic mixtures. When evaluated on 15 mixtures measured with Attenuated Total Reflectance (ATR) IR spectrometer, whose response differs markedly from the training domain, it still achieves 52.0% Top-10 accuracy, evidencing strong cross-instrument transferability. The ability to identify signals of individual molecules within complex spectra extends machine-learning-assisted spectroscopy from idealised samples to realistic laboratory scenarios. All code and pretrained weights are released to accelerate adoption and further development. This advance opens the door to automated structure elucidation using IR data in fields ranging from environmental monitoring to pharmaceutical quality control.