Modern data-driven tools are transforming application-specific polymer development cycles.
Surrogate models that can be trained to predict properties of polymers are becoming
commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge
available in datasets, which are oftentimes sparse; inherent correlations between different
property datasets are disregarded. Here, we demonstrate the potency of multi-task learning
approaches that exploit such inherent correlations effectively. Data pertaining to 36 different …