arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2301.03030cond-mat.mtrl-scicond-mat.softphysics.app-phphysics.comp-ph
收藏

基于物理描述符的可解释机器学习探索高热导率聚合物

Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors

Xiang Huang, Shengluo Ma, C. Y. Zhao, Hong Wang, Shenghong Ju

AI总结:

本研究提出一种基于可解释机器学习和物理特征工程的高通量筛选框架,通过分子动力学模拟数据和物理描述符预测聚合物热导率,发现了107种高热导率聚合物结构,并揭示了其微观物理机制。

摘要:

高效且经济地开发高热导率聚合物对于解决有机器件的散热问题至关重要。目前,由于合成和表征过程中的多自由度,功能性热导率聚合物的实验制备仍是一个试错过程。在本研究中,我们提出了一种通过可解释机器学习和物理特征工程筛选高热导率聚合物链的高通量筛选框架。首先通过分子动力学模拟收集用于训练的聚合物热导率数据集。受类药小分子表示和分子力场启发,计算了320个聚合物单体描述符,并通过分层降维提取了20个具有物理意义的优化描述符。所有机器学习模型的预测精度R2均大于0.80,优于传统图描述符所表示的精度。此外,识别了截面积和二面角刚度描述符对热导率的正/负贡献,获得了107种热导率大于20.00 W/mK的有前景的聚合物结构。还通过符号回归构建了预测聚合物热导率的数学公式。这些高热导率聚合物结构大多为π共轭结构,其重叠的p轨道使其易于保持强链刚度和大的群速度。所提出的数据驱动框架应有助于具有理想性能的聚合物的理论和实验设计。

英文摘要:

The efficient and economical exploitation of polymers with high thermal conductivity is essential to solve the issue of heat dissipation in organic devices. Currently, the experimental preparation of functional thermal conductivity polymers remains a trial and error process due to the multi-degrees of freedom during the synthesis and characterization process. In this work, we have proposed a high-throughput screening framework for polymer chains with high thermal conductivity via interpretable machine learning and physical-feature engineering. The polymer thermal conductivity datasets for training were first collected by molecular dynamics simulation. Inspired by the drug-like small molecule representation and molecular force field, 320 polymer monomer descriptors were calculated and the 20 optimized descriptors with physical meaning were extracted by hierarchical down-selection. All the machine learning models achieve a prediction accuracy R2 greater than 0.80, which is superior to that of represented by traditional graph descriptors. Further, the cross-sectional area and dihedral stiffness descriptors were identified for positive/negative contribution to thermal conductivity, and 107 promising polymer structures with thermal conductivity greater than 20.00 W/mK were obtained. Mathematical formulas for predicting the polymer thermal conductivity were also constructed by using symbolic regression. The high thermal conductivity polymer structures are mostly π-conjugated, whose overlapping p-orbitals enable easily to maintain strong chain stiffness and large group velocities. The proposed data-driven framework should facilitate the theoretical and experimental design of polymers with desirable properties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑