We used these data to train the ML-based quantitative structure-property relationship (QSPR) models. As shown in Figure , chemically interpretable molecular descriptors were generated for the polymers and the dataset was partitioned into a training set used to generate the models, and a test set used to validate their predictive power (details in Methods). Multiple linear regression with expectation maximization (MLREM) algorithms and Bayesian regularized artificial neural network with a Laplacian prior (BRANNLP) algorithms were utilized by the ML models. The measured PL properties served as the prediction targets.