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Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models.

Yufeng Liu1, Zhenyu Li1

  • 1Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui230026, China.

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Predicting molecular ionization energy (IE) is crucial. Graph-based machine learning models, like AttentiveFP, outperform conventional methods, especially for radical molecules, offering efficient quantitative structure-property relationship (QSPR) predictions.

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Machine learning in chemistry

Background:

  • Ionization energy (IE) is a key molecular property.
  • Accurate IE prediction is vital for various chemical applications.
  • Machine learning (ML) offers efficient quantitative structure-property relationship (QSPR) approaches.

Purpose of the Study:

  • To systematically compare ML models for predicting molecular IE.
  • To evaluate the impact of molecular descriptors and model types on IE prediction accuracy.
  • To identify optimal models for reliable IE prediction.

Main Methods:

  • Utilized Mordred and PaDEL for generating molecular descriptors.
  • Implemented conventional ML models, including Support Vector Regression (SVR).
  • Employed graph-based ML models, specifically AttentiveFP.
  • Included a descriptor to identify radical molecules.

Main Results:

  • Support Vector Regression (SVR) emerged as the top-performing conventional ML model.
  • AttentiveFP demonstrated superior performance compared to SVR.
  • The inclusion of a radical descriptor significantly enhanced model performance.
  • Graph-based models excelled in predicting IE for radical molecules.

Conclusions:

  • AttentiveFP offers a high-performance model for IE prediction.
  • Graph-based models provide better insights into radical molecule properties.
  • The study offers valuable guidance for selecting appropriate QSPR models for IE prediction.