The glass transition temperature ( \(\:{T}_{g}\) ) is a pivotal parameter for amorphous polymers, influencing their behavior under different thermal properties. Assessing \(\:{T}_{g}\) is crucial for evaluating material performance across varying temperatures. Our research integrates machine learning with cheminformatics framework to analyze \(\:{T}_{g}\) values across a broad polymer dataset. This framework improves a comprehensive understanding of the quantitative correlations between the polymers’ structural attributes and their \(\:{T}_{g}\) . Utilizing a dataset of 250 polymers, we constructed a series of Machine Learning-based Quantitative Structure-Property Relationship (ML-QSPR) models. The initial ML-QSPR used Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods to identify an optimal set of molecular descriptors. Subsequently, various non-linear machine learning methods, including Support Vector Regression (SVR), Gaussian Process Regression (GPR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were used for comparative purposes and predictive analysis. The results demonstrate that MLP, driven by seventeen (17) selected descriptors, yields the most accurate ML model, with training and external validation \(\:{R}^{2}\:\) values of 0.82 and 0.79, respectively. This model predicts with good performance the \(\:{T}_{g}\) values of the polymers under study, showing the role of specific structural descriptors in refining polymer property predictions.
