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