Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has become a vital field 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 combined with active learning (AL) to uncover polymer blends with enhanced thermal conductivity (TC) compared to the constituent single-component polymers. Initially, the TC of about 600 amorphous single-component polymers and 200 amorphous polymer blends with varying blending ratios are determined through MD simulations. The optimal representation method for polymer blends is 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, is employed to explore the TC of approximately 550,000 unlabeled 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 an odds ratio calculation, emphasizing the impact of increasing R_g and H-bond interactions on enhancing polymer blend TC.

聚合物在从日常使用到先进技术的各种应用中发挥着不可或缺的作用。在机器学习（ML）时代，高分子信息学已成为高效设计和开发高分子材料的重要领域。然而，聚合物信息学的焦点主要集中在单组分聚合物上，而聚合物共混物的广阔化学空间相对尚未得到探索。这项研究采用高通量分子动力学 (MD) 模拟与主动学习 (AL) 相结合，揭示了与单组分聚合物相比导热性 (TC) 增强的聚合物共混物。最初，通过 MD 模拟确定了约 600 种非晶单组分聚合物和 200 种不同混合比例的非晶聚合物共混物的 TC。确定了聚合物共混物的最佳表示方法，该方法涉及加权和方法，将现有的聚合物表示从单组分聚合物扩展到聚合物共混物。采用结合 MD 模拟和 ML 的 AL 框架来探索大约 550,000 种未标记聚合物共混物的 TC。事实证明，AL 框架在加速用于热传输的高性能聚合物共混物的发现方面非常有效。此外，我们还深入研究了 TC、回转半径 ( R _g ) 和氢键之间的关系，强调了链间和链内相互作用在无定形聚合物共混物热传输中的作用。通过对数线性模型和比值比计算揭示了 TC 和R _g改善之间的显着正相关以及氢键相互作用对 TC 增强的间接贡献，强调了增加R _g和氢键相互作用对增强的影响聚合物共混物 TC.