SMARTERS uses an Attention U-Net to predict planar molecular structures directly from simulated TERS hyperspectral images, reaching a mean test Dice of 0.842
The work introduces SMARTERS, an Attention U-Net encoder-decoder trained on simulated TERS hyperspectral images of 1,840 planar small molecules, which maps vibrational spectral images directly to 2D atomic position maps (mean test Dice similarity coefficient 0.842) and to chemically resolved maps per element (H, C, N, O; mean test 0.810), showing that automated molecular structure identification from TERS images is feasible on simulated data without conventional manual comparison and case-by-case quantum-chemistry calculations.