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在高智力要求的领域中，接受过大量数据训练的机器智能已被证明可匹敌甚至超越人类的能力，将其应用于新材料研发中有望节省大量的时间和经济成本。计算分子设计的目标是，按任意给定的物理化学性质要求，确定有希望的新的分子。虽然新近机器学习在从头分子设计方面取得了显著进展，加速了创新材料的发现，但是，其实际效益在应用中，尤其在聚合物科学中，仍未得到证实。
来自日本国立物质材料科学研究所（NIMS）的Junko Morikawa和Ryo Yoshida领导的团队在近期的工作中表明，将机器学习方法与聚合物性能数据库、有机合成专业技术和热性能先进测量技术紧密结合，可以快速、高效地发现新型高热导率聚合物材料。基于聚合物性能数据库，他们首先训练了一个机器学习模型，并基于该模型筛选出了上千种高热导率聚合物材料。进一步以可合成性和易加工性做为依据，最终选择合成了其中的三种。这些新型聚合物实际测量的热导率与机器学习预测值高度一致，且性能与非复合热塑性塑料中最优的聚合物相当。该研究所提出的基于机器学习的逆向设计方法可望进一步推广用于寻找其他目标性能的新型聚合物。
该文近期发表于npj Computational Materials 5: 5 (2019)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm Stephen Wu, Yukiko Kondo, Masa-aki Kakimoto, Bin Yang, Hironao Yamada, Isao Kuwajima, Guillaume Lambard, Kenta Hongo, Yibin Xu, Junichiro Shiomi, Christoph Schick, Junko Morikawa & Ryo Yoshida 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.
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