PT Journal
AU Kamran, J
   Hniopek, J
   Bocklitz, T
TI Transfer-Learning Deep Raman Models Using Semiempirical Quantum Chemistry
SO Journal of Chemical Information and Modeling
JI JCIM
PD June
PY 2025
BP 6632
EP 6643
VL 65
IS 13
PU American Chemical Society
DI 10.1021/acs.jcim.5c00513
WP https://www.db-thueringen.de/receive/dbt_mods_00066724
LA en
SN 1549-9596
AB Biophotonic technologies such as Raman spectroscopy are powerful tools for obtaining highly specific molecular information. Due to its minimal sample preparation requirements, Raman spectroscopy is widely used across diverse scientific disciplines, often in combination with chemometrics, machine learning (ML), and deep learning (DL). However, Raman spectroscopy lacks large databases of independent Raman spectra for model training, leading to overfitting, overestimation, and limited model generalizability. We address this problem by generating simulated vibrational spectra using semiempirical quantum chemistry methods, enabling the efficient pretraining of deep learning models on large synthetic data sets. These pretrained models are then fine-tuned on a smaller experimental Raman data set of bacterial spectra. Transfer learning significantly reduces the computational cost while maintaining performance comparable to models trained from scratch in this real biophotonic application. The results validate the utility of synthetic data for pretraining deep Raman models and offer a scalable framework for spectral analysis in resource-limited settings.
PI Washington, DC
ER