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Understanding the patterns that neural networks learn from chemical spectra

Published:2023 Issue:6 Volume:2 Page:1957-1968
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Rieger Laura Hannemose¹^ORCID,Wilson Max²,Vegge Tejs¹^ORCID,Flores Eibar³^ORCID

Affiliation:

1. Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark

2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

3. Sustainable Energy Technology, SINTEF Industry, Sem Sælands Vei 12, Trondheim, 7034, Norway

Abstract

We train a convolutional neural network to classify functional groups from infrared spectra. With explainability methods, we show the model uses the presence and absence of peaks, at fundamental and anharmonic frequencies for accurate classification.

Funder

H2020 Future and Emerging Technologies

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00203A

Reference59 articles.

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3. M. H.Penner , Food Analysis , Springer , 2017 , pp. 79–88

4. J. I.Steinfeld , Molecules and Radiation: An Introduction to Modern Molecular Spectroscopy , Courier Corporation , 2012

5. Spectral Database Systems: A Review

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1. Correction: Understanding the patterns that neural networks learn from chemical spectra;Digital Discovery;2024