Multi-task and Uncertainty Prediction of Polymer Properties with Graph Network

Polymer-based products are widely used in our daily lives in various forms such as packaging, automobiles, etc. In energy storage technologies, polymer-based dielectric materials are crucial due to their low cost and great thermal as well as electrical properties. However, efficient screening of polymers is challenging due to combinatorial design space. Recently, machine learning based methods have gained great amount of traction due to their ability to model complex systems. However, these methods suffer from certain disadvantages in the way they represent the molecular features and also their inability to model multiple properties simultaneously, while quantifying the uncertainties in the predicted properties. Here, we propose a graph-based Bayesian multi-task learning model to inherently capture the relation between multiple properties for a given polymer candidate. Also, the trained model possesses advanced learning capabilities due to graph-based feature representation and uncertainty quantified predictions.