Bridging the Computational–Experimental Domain Gap: An Autoencoder-Based Machine Learning Workflow for Raman Spectra Characterization of Amino Acid Mixtures
- DOI
- 10.1021/acs.jpca.6c03296
- Published
- 2026-08-10
- Container
- The Journal of Physical Chemistry A
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jpca.6c03296,
title = {Bridging the Computational–Experimental
Domain
Gap: An Autoencoder-Based Machine Learning Workflow for Raman Spectra
Characterization of Amino Acid Mixtures},
author = {Sheng-Hsuan Hung and Yu-Huan Huang and Zong-Rong Ye and Berlin Chen and Yi-Hsin Liu and Ming-Kang Tsai},
year = {2026},
journal = {The Journal of Physical
Chemistry A},
doi = {10.1021/acs.jpca.6c03296},
url = {https://doi.org/10.1021/acs.jpca.6c03296}
}RIS
TY - JOUR TI - Bridging the Computational–Experimental Domain Gap: An Autoencoder-Based Machine Learning Workflow for Raman Spectra Characterization of Amino Acid Mixtures AU - Sheng-Hsuan Hung AU - Yu-Huan Huang AU - Zong-Rong Ye AU - Berlin Chen AU - Yi-Hsin Liu AU - Ming-Kang Tsai PY - 2026 JO - The Journal of Physical Chemistry A DO - 10.1021/acs.jpca.6c03296 UR - https://doi.org/10.1021/acs.jpca.6c03296 ER -
APA
Hung, S., Huang, Y., Ye, Z., Chen, B., Liu, Y., & Tsai, M. (2026). Bridging the Computational–Experimental Domain Gap: An Autoencoder-Based Machine Learning Workflow for Raman Spectra Characterization of Amino Acid Mixtures. The Journal of Physical Chemistry A. https://doi.org/10.1021/acs.jpca.6c03296
Source records
- crossref · retrieved 2026-09-25T22:14:23.176Z