Paired Neural Network for Matching Experimental and Predicted Infrared Spectra

Here, we present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on in silico techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity.. Authors: Colby, Sean M. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]; Bade, Jessica L. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000175950017); Jystad, Amy M. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000210659613); Rice, Peter S. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000329463781); Ibrahim, Yehia M. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:000000016085193X). DOE Contract: AC05-76RL01830. Subjects: Neural Network; in silico; infrared (IR) spectroscopy; m/q; m/q Initiative; machine learning (ML); molecular identification; spectra