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Infrared Spectra Prediction for Diazo Groups Utilizing a Machine Learning Approach with Structural Attention Mechanism

Chengchun Liu, Fanyang Mo

TL;DR

Infrared spectroscopy provides molecular fingerprints but predicting spectra and interpreting structure–property relationships remains challenging. The authors introduce a Structural Attention Mechanism descriptor (SAMD) to prioritize chemical information near functional groups and combine it with Morgan fingerprints, integrating an ensemble stacking–voting approach to predict IR spectra of diazo compounds. The method achieves high predictive accuracy (cross-validated $R^2$ around $0.969$), demonstrates robustness across molecular similarity and noise, and even extrapolates to unstable species like diazomethane with close agreement to experimental and theoretical benchmarks; SHAP analysis reveals chemically meaningful descriptors (notably carbonyl count and the atom attached to the diazo group) as key drivers. This work provides a scalable, interpretable framework for spectroscopic prediction in complex molecules and paves the way for extending SAM-guided predictions to other functional groups and broader spectroscopic contexts.

Abstract

Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. However, the intricate molecular fingerprints characterized by unique vibrational and rotational patterns present substantial analytical challenges. Here, we present a machine learning approach employing a Structural Attention Mechanism tailored to enhance the prediction and interpretation of infrared spectra, particularly for diazo compounds. Our model distinguishes itself by honing in on chemical information proximal to functional groups, thereby significantly bolstering the accuracy, robustness, and interpretability of spectral predictions. This method not only demystifies the correlations between infrared spectral features and molecular structures but also offers a scalable and efficient paradigm for dissecting complex molecular interactions.

Infrared Spectra Prediction for Diazo Groups Utilizing a Machine Learning Approach with Structural Attention Mechanism

TL;DR

Infrared spectroscopy provides molecular fingerprints but predicting spectra and interpreting structure–property relationships remains challenging. The authors introduce a Structural Attention Mechanism descriptor (SAMD) to prioritize chemical information near functional groups and combine it with Morgan fingerprints, integrating an ensemble stacking–voting approach to predict IR spectra of diazo compounds. The method achieves high predictive accuracy (cross-validated around ), demonstrates robustness across molecular similarity and noise, and even extrapolates to unstable species like diazomethane with close agreement to experimental and theoretical benchmarks; SHAP analysis reveals chemically meaningful descriptors (notably carbonyl count and the atom attached to the diazo group) as key drivers. This work provides a scalable, interpretable framework for spectroscopic prediction in complex molecules and paves the way for extending SAM-guided predictions to other functional groups and broader spectroscopic contexts.

Abstract

Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. However, the intricate molecular fingerprints characterized by unique vibrational and rotational patterns present substantial analytical challenges. Here, we present a machine learning approach employing a Structural Attention Mechanism tailored to enhance the prediction and interpretation of infrared spectra, particularly for diazo compounds. Our model distinguishes itself by honing in on chemical information proximal to functional groups, thereby significantly bolstering the accuracy, robustness, and interpretability of spectral predictions. This method not only demystifies the correlations between infrared spectral features and molecular structures but also offers a scalable and efficient paradigm for dissecting complex molecular interactions.
Paper Structure (17 sections, 5 figures)

This paper contains 17 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Results and discussion
  3. Dataset construction and feature engineering
  4. Model construction and performance evaluation
  5. Model robustness and generalization analysis
  6. Model decision-making process and interpretability analysis
  7. Conclusions
  8. ASSOCIATED CONTENT
  9. Data Availability
  10. Supporting Information
  11. AUTHOR INFORMATION
  12. Corresponding Author
  13. Author
  14. Author Contributions
  15. Notes
  16. ...and 2 more sections

Figures (5)

  • Figure 1: The workflow of this study.(A) Database creation workflow. (B) Feature engineering workflow. (C) Machine learning prediction.
  • Figure 2: Model construction and performance evaluation.(A) Evaluation of model performance based on some common machine learning algorithms. (B) Performance evaluation of mixture model. (C) Evaluation of the impact of different training data on model performance.
  • Figure 3: Model Robustness Analysis.(A) The influence of similarity on the prediction model. (B) The influence of noise data on the prediction model.
  • Figure 4: Model Interpretability Analysis.(A) Feature importance analysis (top 10 features) based on average SHAP value. (B) Analysis of Sample Distribution Utilizing SHAP Values. Empirical analysis indicates that the feature O_DOUBLE_R2 holds predominant significance, on average, in the context of infrared absorption. Notably, its characteristic value is identified as 0 (denoted in blue), suggesting that the corresponding wavenumber is less likely to exceed the average threshold. (C) Pearson correlation analysis between the infrared wave number of the diazo group and the number of carbonyl groups. (D) Featurization of arbitrarily selected molecules utilizing a structural attention mechanism (SAM). (E) Model of decision-making process informed by SHAP values. This diagram presents a hierarchical analysis, beginning from the base with the mean wave number. Progressing upwards, it delineates how various features sequentially influence the model's decisions. The apex of the structure illustrates the ultimate predicted infrared (IR) value derived from this cumulative assessment. (F) A force diagram using SHAP values measures how different features affect the model's decisions. Features that increase the wave number are on the left, and those that decrease it are on the right. The model's final prediction is 2082.0.
  • Figure :