Transformer Models in Vibrational Spectroscopy: A Review
- 1Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- 2Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
Abstract
Transformer models have attracted increasing interest in vibrational spectroscopy because of their ability to capture long-range dependencies in spectral data. These models have been applied to analytical tasks including spectral preprocessing, classification, quantitative analysis, multimodal data fusion, and spectra-to-structure prediction. Their flexible architectures support applications in Raman, infrared (IR), near-infrared (NIR), and surface-enhanced Raman spectroscopy (SERS), with examples across chemistry, biology, and materials science. Transformer-based models also offer opportunities to integrate chemical, physical, and biological information in spectral analysis and support interdisciplinary research involving chemometrics, deep learning, and experimental spectroscopy. In this review, we elucidate the fundamental principles of Transformer models and their wide-ranging applications in vibrational spectroscopy, spanning from baseline correction and deconvolution to molecular identification, disease diagnosis, food authentication, and quantitative property prediction. Finally, as Transformer models may not be suitable for every analytical task or consistently outperform simpler approaches, we discuss current challenges and future trends in Transformer models in vibrational spectroscopy and related analytical sciences.
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BibTeX
@article{jafari2026transformer,
title = {{Transformer Models in Vibrational Spectroscopy: A Review}},
author = {Jamile Mohammad Jafari and Thomas Bocklitz},
journal = {Molecules},
year = {2026},
month = oct,
doi = {10.3390/molecules31203745},
url = {https://www.mdpi.com/1420-3049/31/20/3745},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Transformer Models in Vibrational Spectroscopy: A Review
AU - Jamile Mohammad Jafari
AU - Thomas Bocklitz
PY - 2026
DA - 2026/10/01
JO - Molecules
DO - 10.3390/molecules31203745
UR - https://www.mdpi.com/1420-3049/31/20/3745
AB - Transformer models have attracted increasing interest in vibrational spectroscopy because of their ability to capture long-range dependencies in spectral data. These models have been applied to analytical tasks including spectral preprocessing, classification, quantitative analysis, multimodal data fusion, and spectra-to-structure prediction. Their flexible architectures support applications in Raman, infrared (IR), near-infrared (NIR), and surface-enhanced Raman spectroscopy (SERS), with examples across chemistry, biology, and materials science. Transformer-based models also offer opportunities to integrate chemical, physical, and biological information in spectral analysis and support interdisciplinary research involving chemometrics, deep learning, and experimental spectroscopy. In this review, we elucidate the fundamental principles of Transformer models and their wide-ranging applications in vibrational spectroscopy, spanning from baseline correction and deconvolution to molecular identification, disease diagnosis, food authentication, and quantitative property prediction. Finally, as Transformer models may not be suitable for every analytical task or consistently outperform simpler approaches, we discuss current challenges and future trends in Transformer models in vibrational spectroscopy and related analytical sciences.
SN - 1420-3049
ER -