CCL

Research note

Analytical Chemistry

Infrared Spectra Prediction for Functional Group Region Utilizing a Machine Learning Approach With Structural Neighboring Mechanism

Chengchun Liu, Ruqiang Zou, and Fanyang Mo

A functional-group-centered learning strategy that uses local structural context to improve the accuracy, robustness, and interpretability of infrared-spectrum prediction.

10.1021/acs.analchem.4c01972 View source code

01 / Problem

Problem

A functional group’s infrared response depends not only on its identity but also on the surrounding molecular environment, making context-aware prediction important.

02 / Approach

Approach

The method represents the structural neighborhood around a target functional group and uses that local chemical context in a machine-learning prediction workflow.

03 / Why it matters

Why it matters

Centering the representation on chemically meaningful neighborhoods connects spectral prediction more directly to the structural context chemists use to interpret IR signals.

04 / Publication details

Publication details

Authors
Chengchun Liu, Ruqiang Zou, and Fanyang Mo
Journal
Analytical Chemistry
Year

BibTeX

@article{liu2024irprediction,
  title   = {Infrared Spectra Prediction for Functional Group Region Utilizing a Machine Learning Approach With Structural Neighboring Mechanism},
  author  = {Chengchun Liu and Ruqiang Zou and Fanyang Mo},
  journal = {Analytical Chemistry},
  year    = {2024},
  date    = {2024-09-19},
  volume  = {96},
  number  = {39},
  pages   = {15550--15562},
  doi     = {10.1021/acs.analchem.4c01972},
  url     = {https://doi.org/10.1021/acs.analchem.4c01972}
}