Objective:

To develop a deep learning model that reconstructs molecular geometries and identifies chemical elements from simulated tip-enhanced Raman spectroscopy (TERS) images.

Approach:

• Model Development: The model, named SMARTERS, uses an Attention U-Net encoder-decoder architecture to convert TERS hyperspectral image cubes into atomic maps.
• Data Generation: Training data were generated from density functional theory calculations and TERS simulations, focusing on 1,840 planar molecules from an initial set of 28,570.
• Performance Metrics: SMARTERS achieved a mean Dice similarity coefficient of 0.842 on the test set for atomic-position prediction.

Key Findings:

• SMARTERS recorded precision and recall of 0.98, a mean atom-count error of 0.38, and a coordinate root mean square deviation of 0.097 Å.
• A second model predicted both atomic positions and elemental identities, achieving a mean test-set Dice score of 0.810.
• Performance declined for non-planar molecules due to weaker signals from atoms farther from the tip.

Interpretation:

SMARTERS shows variability in performance based on molecular geometry and training data composition.

Limitations:

• SMARTERS performed poorly on experimental TERS images, with predicted atomic positions not resembling the actual molecular structure.
• Differences between simplified simulations and experimental conditions, such as tip geometry and substrate effects, contributed to the limitations.

Conclusion:

Significant gaps were identified when applying the current approach to experimental data.

Sources:

• PRX Intelligence