  The success of the Materials Genome Initiative has led to opportunities for
data-driven approaches for materials discovery. The recent development of
Polymer Genome (PG), which is a machine learning (ML) based data-driven
informatics platform for polymer property prediction, has significantly
increased the efficiency of polymer design. Nevertheless, continuous expansion
of the "training data" is necessary to improve the robustness, versatility and
accuracy of the ML predictions. In order to test the performance and
transferability of the predictive models presently available in PG (which were
previously trained on a dataset of 450 polymers), we have carefully collected
additional experimental glass transition temperature (Tg) data for 871 polymers
from multiple data sources. The Tg values predicted by the present PG models
for the polymers in the newly collected dataset were compared directly with the
experimental Tg to estimate the accuracy of the present model. Using the full
dataset of 1321 polymers, a new ML model for Tg was built following past work.
The RMSE of prediction for the extended dataset, when compared to the earlier
one, decreased to 27 K from 57 K. To further improve the performance of the Tg
prediction model, we are continuing to accumulate new data and exploring new ML
approaches.
