Automatic materials characterization from infrared spectra using convolutional neural networks

Abstract

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 37 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.