Journal article
Classification of Samples via Neural-Network Augmented Two-Dimensional Infrared Spectroscopy
The journal of physical chemistry. B, Vol.129(19), pp.4738-4746
05/15/2025
DOI: 10.1021/acs.jpcb.4c08573
PMCID: PMC12086836
PMID: 40311107
Appears in UI Libraries Support Open Access
Abstract
The application of artificial neural network (ANN) techniques to spectroscopy has proven to be a powerful tool for the rapid and accurate classification of experimental samples. However, despite the unique abilities of two-dimensional infrared spectroscopy (2D IR), the use of ANNs to classify samples on the basis of their 2D-IR spectra has been unexplored. We present two investigations into utilizing ANNs to perform end-to-end classification of samples from their 2D-IR spectra. In the first, we construct a model that can perform a binary classification of experimental samples on the basis of their solvent. In the second, we demonstrate that classification is possible even for a single spectral slice of pump-delay and waiting-time combination even when samples display almost identical spectra. These results clearly demonstrate the potential of ANN-augmented 2D IR, with particular emphasis on its use as a technology for high-throughput screening applications.
Details
- Title: Subtitle
- Classification of Samples via Neural-Network Augmented Two-Dimensional Infrared Spectroscopy
- Creators
- Evan B Schroeder - University of Iowa, ChemistryChristopher M Cheatum - University of Iowa, Chemistry
- Resource Type
- Journal article
- Publication Details
- The journal of physical chemistry. B, Vol.129(19), pp.4738-4746
- DOI
- 10.1021/acs.jpcb.4c08573
- PMID
- 40311107
- PMCID
- PMC12086836
- NLM abbreviation
- J Phys Chem B
- ISSN
- 1520-6106
- eISSN
- 1520-5207
- Publisher
- American Chemical Society
- Grant note
- Division of Chemistry: CHE-2102048 National Science Foundation
The authors gratefully acknowledge support for this work from the National Science Foundation under Award Number CHE-2102048.
- Language
- English
- Electronic publication date
- 05/01/2025
- Date published
- 05/15/2025
- Academic Unit
- Liberal Arts and Science Admin; Chemistry
- Record Identifier
- 9984815517802771
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