The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real-world applications, particularly in polymer science. We demonstrate the successful discovery of new polymers with high thermal conductivity, inspired by machine-learning-assisted polymer chemistry. This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data, expertise from laboratory synthesis and advanced technologies for thermophysical property measurements. Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties, we identified thousands of promising hypothetical polymers. From these candidates, three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications. The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK, which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.

在计算分子设计中使用机器学习具有极大的潜力来加速发现创新材料。但是，它的实际好处在现实世界的应用程序中仍然没有得到证实，特别是在聚合物科学中。我们展示了受机器学习辅助的聚合物化学的启发，成功发现了具有高导热率的新型聚合物。这项发现是通过在数量有限的聚合物性质数据上受训的机器智能，实验室合成的专业知识以及热物理性质测量的先进技术之间的相互作用而实现的。使用受过训练的分子设计算法来识别定量结构-相对于热导率和其他目标聚合物特性的性质关系，我们确定了数千种有前途的假设聚合物。从这些候选物中选择了三个用于单体合成和聚合，因为它们具有合成易接近性，并且具有易于在进一步应用中进行加工的潜力。合成的聚合物的导热系数为0.18-0.41 W / mK，与非复合热塑性塑料中的最新聚合物相当。