Active Learning Exploration of Thermally Conductive Strained Polymers

Thermal conductivity (TC), as an important transport property of polymers, can be improved when subject to strain since it can help align polymer chains. However, discovery of polymers that may have high TC after strain can be time-consuming and without guarantee of success. In this work, we employ an active learning scheme to speed up the discovery of high TC polymers. Polymers under strain were simulated using molecular dynamics (MD) and their TC are calculated. A Bayesian Neural Network (BNN) is then trained using these data. The BNN is used to screen the PoLyInfo database, and predicted mean TC and uncertainty are used towards an acquisition function to recommend polymers for MD labeling. The TC of these selected polymers is then calculated using MD simulations. The obtained data are then added to the training set to start another iteration in the active learning cycle. Through a few cycles, we were able to identify strained polymers with TC much better than the original dataset.