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Explainable Machine Learning for Characterizing Unknown Molecular Structures in Infrared Spectra

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SSIN achieved a detection accuracy greater than 0.920 on the NIST database, highlighting its effectiveness in infrared spectra analysis.
The model characterized unknown molecular structures by accurately identifying absorption peaks linked to functional groups.
Results showed that human-readable reports generated by SSIN had an accuracy of 81-99%, improving the interpretability of machine learning predictions.
This approach mitigates the limitations of existing machine learning methods, combining prior knowledge with explainable deep learning techniques.

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Deep learning method optimizes detection accuracy and improves explainability in infrared spectra analysis, addressing previous limitations.

Na et al. (2025) studied this question.

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Deep learning method optimizes detection accuracy and improves explainability in infrared spectra analysis, addressing previous limitations.