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<p>Machine learning (ML) has emerged as a powerful tool for predicting polymer properties, including glass transition temperature (<i>T</i><sub>g</sub>), which is a critical factor influencing polymer applications. In this study, a dataset of polymer structures and their <i>T</i><sub>g</sub> values were created and represented as adjacency matrices based on molecular graph theory. Four key structural descriptors, flexibility, side chain occupancy length, polarity, and hydrogen bonding capacity, were extracted and used as inputs for ML models: Extra Trees (ET), Random Forest (RF), Gaussian Process Regression (GPR), and Gradient Boosting (GB). Among these, ET and GPR achieved the highest predictive performance, with <i>R</i><sup>2</sup> values of 0.97, and mean absolute errors (MAE) of approximately 7–7.5 K. The use of these extracted features significantly improved the prediction accuracy compared to previous studies. Feature importance analysis revealed that flexibility had the strongest influence on <i>T</i><sub>g</sub>, followed by side-chain occupancy length, hydrogen bonding, and polarity. This work demonstrates the potential of data-driven approaches in polymer science, providing a fast and reliable method for <i>T</i><sub>g</sub> prediction that does not require experimental inputs.</p>

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Machine Learning-assisted Prediction of Polymer Glass Transition Temperature: A Structural Feature Approach

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Bardia Afsordeh,
  • Hadi Shirali

摘要

Machine learning (ML) has emerged as a powerful tool for predicting polymer properties, including glass transition temperature (Tg), which is a critical factor influencing polymer applications. In this study, a dataset of polymer structures and their Tg values were created and represented as adjacency matrices based on molecular graph theory. Four key structural descriptors, flexibility, side chain occupancy length, polarity, and hydrogen bonding capacity, were extracted and used as inputs for ML models: Extra Trees (ET), Random Forest (RF), Gaussian Process Regression (GPR), and Gradient Boosting (GB). Among these, ET and GPR achieved the highest predictive performance, with R2 values of 0.97, and mean absolute errors (MAE) of approximately 7–7.5 K. The use of these extracted features significantly improved the prediction accuracy compared to previous studies. Feature importance analysis revealed that flexibility had the strongest influence on Tg, followed by side-chain occupancy length, hydrogen bonding, and polarity. This work demonstrates the potential of data-driven approaches in polymer science, providing a fast and reliable method for Tg prediction that does not require experimental inputs.