[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123869-en":3,"doc-seo-123869-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123869,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Exploring High Thermal Conductivity Polymers via Interpretable Machine Learning with Physical Descriptors","Efficient, economical discovery of high thermal conductivity polymers is crucial for heat dissipation in organic devices, yet experimental synthesis and characterization are often slow trial-and-error tasks due to many coupled degrees of freedom. Polymer informatics aims to combine data science, machine learning, and experiments/simulations, but reliable datasets are limited and polymer representation remains difficult. A high-throughput, interpretable framework is proposed using molecular-dynamics-trained models, optimized physical monomer descriptors, interpretable feature contributions, and symbolic-regression prediction formulas to identify promising high-thermal-conductivity polymer structures.","Exploring High Thermal Conductivity Polymers via Interpretable Machine Learning with Physical Descriptors  \nXiang Huang1,†, Shengluo Ma1,†, C. Y. Zhao1, Hong Wang2, and Shenghong Ju1, 2, *  \n1 China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai, China  \n2 Materials Genome Initiative Center, School of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, China  \n†Equal contribution  \nABSTRACT  \nThe 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. Polymer informatics, which efficiently combines data science, machine learning, and polymer experiment/simulation, leading to the efficient design of polymer materials with desired properties. However, available polymer thermal conductivity databases are rare, and establishing appropriate polymer representation is still challenging. In this work, we have proposed a highthroughput 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.  \n* Corresponding author: [shenghong.ju@sjtu.edu.cn](shenghong.ju@sjtu.edu.cn).  \n1. INTRODUCTION  \nPolymers are extensively used in industry and daily life, owing to various advantages of chemical inertness, mechanical flexibility and light weight 1. As the organic electronics are becoming smaller while the power density keeps increasing, the thermal management and heat dissipation capability have attracted significant attention 2. However, conventional polymers are thermal insulators with reported thermal conductivity in the range from 0.1 to 0.5 W/mK, preventing the development of organic electronics 3. Polymers with high thermal conductivity are urgently demanded in organic energy storage and electronic devices to accommodate revolutionary innovations in organic electronics and optoelectronics 4. The polymer morphology and topology were found to be closely related to thermal conductivity 5. Increasing the crystallite orientation and crystallinity can significantly reduce the phonon scattering and enhance the thermal conductivity along the chain directions, which has been demonstrated by both experiments 6-8 and theoretical simulations 9-12. A recent study has fabricated polyethylene (PE) films by disentanglement and alignment of amorphous chains with a metal-like thermal conductivity of 62 W/mK, over two orders of magnitude greater than that of classical amorphous polymers 6. Moreover, molecular dynamics simulations have suggested that individual crystalline PE chains","cbCaiaaeJWEra2IN","https://ap.wps.com/l/cbCaiaaeJWEra2IN","pdf",3628784,1,50,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is finding high thermal conductivity polymers important in organic devices?\",\"answer\":\"High thermal conductivity enables efficient heat dissipation, which is increasingly critical as devices become smaller and power density rises.\"},{\"question\":\"What makes current discovery approaches challenging?\",\"answer\":\"Conventional research often relies on trial-and-error because synthesis and characterization involve multiple degrees of freedom, and the polymer chemical space is extremely large.\"},{\"question\":\"How does the proposed framework make predictions and remain interpretable?\",\"answer\":\"It uses interpretable machine learning with physically meaningful monomer descriptors, identifies feature contributions for positive/negative effects, and constructs prediction formulas using symbolic regression.\"}]","Exploring High Thermal Conductivity Polymers via Interpretable Machine Learning with Physical Descriptors | 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is finding high thermal conductivity polymers important in organic devices?","Question",{"text":75,"@type":76},"High thermal conductivity enables efficient heat dissipation, which is increasingly critical as devices become smaller and power density rises.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes current discovery approaches challenging?",{"text":80,"@type":76},"Conventional research often relies on trial-and-error because synthesis and characterization involve multiple degrees of freedom, and the polymer chemical space is extremely large.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed framework make predictions and remain interpretable?",{"text":84,"@type":76},"It uses interpretable machine learning with physically meaningful monomer descriptors, identifies feature contributions for positive/negative effects, and constructs prediction formulas using symbolic 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