Unlocking Enhanced Thermal Conductivity in Polymer Blends through Active Learning

Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics promises to become a valuable tool for efficiently designing and developing polymeric materials. However, the focus of polymer informatics has predominantly centered on single-component polymers, leaving the vast chemical space of polymer blends relatively unexplored. This study employs a high-throughput molecular dynamics (MD) simulation approach combined with active learning (AL) to uncover polymer blends with enhanced thermal conductivity (TC) compared to the constituent single-component polymers. Initially, TC values for about 600 amorphous single-component polymers and approximately 200 amorphous polymer blends with varying blending ratios are determined through MD simulations. The optimal representation method for polymer blends is also identified, which involves a weighted sum approach that extends existing polymer representation from single-component polymers to polymer blends. An AL framework, combining MD simulation and ML models, is employed to explore the TC of an unlabeled dataset comprising approximately 550,000 potential polymer blends. The AL framework proves highly effective in accelerating the discovery of high-performance polymer blends for thermal transport. Additionally, we delve into the relationship between TC, radius of gyration (R_g), and hydrogen bonding, highlighting the roles of inter- and intra-chain interactions in thermal transport in amorphous polymer blends. A significant positive association between TC and R_g improvement and an indirect contribution from H-bond interaction to TC enhancement are revealed through a log-linear model and odd ratio calculation based on the polymer blend dataset, emphasizing the impact of increasing R_g and H-bond interactions on enhancing polymer blend TC.