The glass transition temperature (Tg) is one of the most important thermal parameters for amorphous polymers, as it defines the temperature range in which a material can be used reliably. However, experimental determination of Tg is time-consuming, costly and difficult to apply across large polymer libraries, making computational prediction methods increasingly attractive. The workflow integrates cheminformatics with machine learning to establish quantitative correlations between the structural attributes of the polymers and their measured Tg values. The developed ML-QSPR framework can help researchers and formulators rapidly estimate Tg values for large sets of candidate polymers, significantly reducing the reliance on labour-intensive experimental screening. Source: Keya, K. N. et al., Comparative evaluation of machine learning-based QSPR techniques for predicting polymer glass transition temperature.