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Machine learning offers promising tools to develop surrogate models forpolymer structure-property relations. Surrogate models can be built uponexisting polymer data and are useful for rapidly predicting the properties ofunknown polymers. The accuracy of such ML models appears to depend on thefeature space representation of polymers, the range of training data, andlearning algorithms. Here, we establish connections between these factors forpredicting the glass transition temperature of polymers. Our analysis suggestslinear models with a smaller number of fitting parameters are as accurate asnonlinear models with a large number of hidden and unexplainable parameters.Also, the performance of a monomer topology-based ML model is found to bequalitatively identical to that of a physicochemical descriptor-based ML model.We find that the transferability of ML models enhances as the property range ofthe training data increases. Moreover, we establish new Tg polymer chemistrycorrelations via ML. Our work illustrates how ML can advance the fundamentalunderstanding of polymer structure-property correlations.

Explainability and Transferability of Machine Learning Models for Predicting the Glass Transition Temperature of Polymers