Machine learning predicts AFM-derived surface morphology latent features from Raman spectra with moderate accuracy (test R2 up to 0.69) and generates Raman/PL spectra across modalities, though quantitative feature extraction from generated spectra is weak.
A comprehensive review on Raman spectroscopy applications
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Cross-Modal Characterization of Thin Film MoS$_2$ Using Generative Models
Machine learning predicts AFM-derived surface morphology latent features from Raman spectra with moderate accuracy (test R2 up to 0.69) and generates Raman/PL spectra across modalities, though quantitative feature extraction from generated spectra is weak.