Functional Group Identification from Infrared Spectra via a Dual-Head Convolutional Network

Area of Science:

• Spectroscopy
• Machine Learning
• Computational Chemistry

Background:

• Infrared (IR) spectroscopy is crucial for molecular characterization.
• Automated functional group identification in IR spectra is hindered by spectral overlap and computational costs.
• Retraining models for new functional groups is often required, demanding efficiency.

Purpose of the Study:

• To develop a computationally efficient and accurate method for automated functional group identification from IR spectra.
• To balance predictive accuracy with practical computational requirements for laboratory deployment.
• To address the limitations of existing methods in handling spectral complexities and retraining needs.

Main Methods:

• A physically motivated dual-head Convolutional Neural Network (CNN) was proposed.
• The CNN jointly models local diagnostic bands and global spectral context.
• The model was evaluated on multiple NIST-derived datasets and external benchmarks.

Main Results:

• The proposed CNN achieved consistent performance gains and strong cross-dataset generalization.
• The model maintained high performance when expanded to 35 functional group classes, showing scalability.
• Feasibility tests confirmed training is possible on standard hardware within practical timeframes.

Conclusions:

• The dual-head CNN offers a robust solution for functional group identification in IR spectroscopy.
• The approach provides reliable generalization and scalability to detailed chemical taxonomies.
• The method is suitable for everyday laboratory deployment due to its efficiency and accuracy.