Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

Understand

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.