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聚合物具有灵活的结构可调性、出色的化学稳定性、卓越的机械延展性和轻质高强等诸多优点被广泛地应用于我们的日常生活和工业生产中。当前，有机电子器件正朝着小型化和集成化方向发展，以及功率密度的不断提高，导致本征聚合物的低导热特性引起的散热矛盾显得格外突出。
Fig. 1 Schematics of high-throughput screening of polymers with high TC via interpretable machine learning. which
同时，因缺乏充足的带有导热系数的聚合物可靠样本量，以及聚合物在合成和表征等方面存在的困难，使我们对聚合物的微观结构与导热性能之间的一般规律，知之甚少。伴随着高性能超级计算集群及人工智能技术发展，大规模多尺度模拟和机器学习为聚合物结构与导热性能的构效关系的把握及高导热聚合物的开发提供了新的方向。
Fig. 2 Visualization of polymer data distribution in a 2D space by UMAP. a, b and c correspond to the selected, PoLyInfo, and PI1M datasets, respectively.
来自上海交通大学中英国际低碳学院鞠生宏副教授团队，提出了一种用于描述复杂聚合物体系的物理特征工程技术，结合多尺度模拟仿真与可解释的机器学习建立了聚合物系统中微观结构构象与导热性能的模型，并揭示了不同层次结构中微观作用与导热强化机制的统一联系。
Fig. 3 Polymer descriptors down-selection and ML models training.
该研究从聚合物单体结构中提取物理描述符，通过递归筛选特征降维后成功建立了高保真信息学模型，既可用于高导热聚合物链的高效筛选，又可揭示不同层次结构的构象、原子作用、电子结构与导热性能的内在联系：1）高导热聚合物链通常有着强的链内原子间相互作用，其对应的非晶无定形聚合物也更易于表现好的导热性能；2）大多高导热聚合物为共轭结构，有利于保持大的链刚度；3）由于聚合物链有着高度一致的链取向，其热导率变化对于二面角旋转导致的链的有序度改变更为敏感。
Fig. 4 Analysis of feature importance using SHAP on RF model trained by optimized descriptors.
Fig. 5 GPSR for TC prediction of promising polymers.
作者所提出的数据驱动框架应有助于理论/实验高效设计具有理想特性的聚合物，以其准确把握聚合物结构与性能间的内在联系。该文近期发表于npj Computational Materials 9:191 (2023)，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。
Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors
Xiang Huang, Shengluo Ma, C. Y. Zhao, Hong Wang & Shenghong Ju
The efficient and economical exploitation of polymers with high thermal conductivity (TC) is essential to solve the issue of heat dissipation in organic devices. Currently, the experimental preparation of functional polymers with high TC remains a trial-and-error process due to the multi-degrees of freedom during the synthesis and characterization process. Polymer informatics equips machine learning (ML) as a powerful engine for the efficient design of polymers with desired properties. However, available polymer TC databases are rare, and establishing appropriate polymer representation is still challenging. In this work, we propose a high-throughput screening framework for polymer chains with high TC via interpretable ML and physical feature engineering. The hierarchical down-selection process stepwise optimizes the 320 initial physical descriptors to the final 20 dimensions and then assists the ML models to achieve a prediction accuracy R2 over 0.80, which is superior to traditional graph descriptors. Further, we analyze the contribution of the individual descriptors to TC and derive the explicit equation for TC prediction using symbolic regression. The high TC polymer structures are mostly π-conjugated, whose overlapping p-orbitals enable easy maintenance of strong chain stiffness and large group velocities. Ultimately, we establish the connections between the individual chains and the amorphous state of polymers. Polymer chains with high TC have strong intra-chain interactions, and their corresponding amorphous systems are favorable for obtaining a large radius of gyration and causing enhanced thermal transport. The proposed data-driven framework should facilitate the theoretical and experimental design of polymers with desirable properties.
