TERS-ABNet: deep learning for single-molecule reconstruction with atomic precision from TERS mapping

Clicks: 11
ID: 333491
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #396 of 416 articles by views in national science review

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 416 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Determining the chemical structure of a single molecule on a surface from spectroscopic data constitutes a challenging high-dimensional inverse problem. While tip-enhanced Raman spectroscopy (TERS) enables chemically specific imaging of single molecules with sub-nanometer spatial resolution, reconstructing complete molecular structures from TERS maps remains difficult due to ambiguous vibrational signatures and heavy reliance on expert interpretation. Here, we introduce TERS-ABNet, a deep-learning framework that reformulates single-molecule structure determination as an image-to-graph inference task. Leveraging a two-track architecture, the model simultaneously predicts probabilistic maps for predefined atom and bond/group classes, enabling the direct construction of explicit atom-bond graphs without dependence on conventional vibrational-mode assignment. Trained on simulated datasets, TERS-ABNet achieves ~94% accuracy in atom-type classification (mean coordinate error: ~0.23 Å), enabling reliable recovery of molecular connectivity and full reconstruction of single-molecule structures from TERS maps. The framework generalizes across diverse spatial resolutions and structural complexities through transfer learning, and successfully retrieves partial atom- and bond-resolved features of a single porphyrin molecule from experimental TERS data. This work establishes a generalizable deep-learning paradigm for inferring explicit atom-bond graph representations from high-dimensional spectroscopic imaging data, offering a transformative pathway towards automated molecular structure determination in nanoscale characterization.
Reference Key
openalex_W7223234094 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jie Cui, Yao Zhang, Yang Zhang, Yi Luo, Zhen-Chao Dong
Are you one of these authors? Claim your profile
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag660
URL
https://doi.org/10.1093/nsr/nwag660
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.