Two CNN models, trained on real color-space images and over 10,000 synthetic bilayer shapes, identify MoS2 thicknesses and predict twist angles, with experimental validation via second-harmonic generation and Raman spectroscopy.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning
Two CNN models, trained on real color-space images and over 10,000 synthetic bilayer shapes, identify MoS2 thicknesses and predict twist angles, with experimental validation via second-harmonic generation and Raman spectroscopy.