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RESEARCH PAPER · Chinese Physics B · 2023

Prediction of lattice thermal conductivity with two-stage interpretable machine learning

Jinlong Hu, Yuting Zuo, Yuzhou Hao, Guoyu Shu, Yang Wang, Minxuan Feng, Xuejie Li, Xiaoying Wang, Jun Sun, Xiangdong Ding, Zhibin Gao, Guimei Zhu, Baowen Li

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42 direct citing works

Prediction of lattice thermal conductivity with two-stage interpretable machine learningOriginalThermal conductivity of sliding bilayer h-BN and its manipulation with strain and layer confinement18Machine Learning Aided Design and Optimization of Thermal Metamaterials195Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors32Lattice thermal conductivity of material and its correlation with Debye temperature using regression models2Machine learning for next-generation thermoelectrics16High efficiency thermal insulation material based on boron hybrid silicone rubber and hollow microspheres7Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network10Machine Learning-Directed Discovery of Ultrahigh Young’s Modulus Crystals0Machine learning prediction of HSE06-level band gaps in two-dimensional semiconductors with reference-guided graph neural networks0Accelerating the discovery of materials with expected thermal conductivity via a synergistic strategy of DFT and interpretable deep learning2Machine Learning for Thermal Transport Prediction in Nanoporous Materials: Progress, Challenges, and Opportunities10Reducing lattice thermal conductivity via phonon engineering: Strategies for high-performance thermoelectrics5Machine learning–driven thermoelectric materials: Review on prediction, optimization, and discovery2Thermal conductivity of carbon nanotubes using nonequilibrium molecular dynamics combined with a machine learning potential2Artificial intelligence and the frontier of phonon engineering: a perspective on discovering extreme thermal materials0Studying Heat Conduction Properties by Using Machine Learning Potentials: a Brief Review0Artificial Intelligence-Enabled Cross-Scale Thermal Design: A Machine-Learning-Centered Review from Atoms to Devices0

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  1. Thermal conductivity of sliding bilayer h-BN and its manipulation with strain and layer confinement

    Journal of Materials Chemistry C · 2023-01-01

    Yi-Ming Zhao, Chun Zhang, Sunmi Shin, Lei Shen

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  2. Machine Learning Aided Design and Optimization of Thermal Metamaterials

    Chemical Reviews · 2024-03-28

    Changliang Zhu, Emmanuel Anuoluwa Bamidele, Xiangying Shen, Guimei Zhu, Baowen Li

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  3. Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

    Advanced Engineering Materials · 2024-06-20

    Zhenyu Yuan, Xueman Luo, Fanli Meng

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  4. Lattice thermal conductivity of material and its correlation with Debye temperature using regression models

    Unknown journal · 2024-06-21

    Sayantan Dandapat, Nepal Sahu, Chandrashekhar Azad, Uday Kumar

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  5. Machine learning for next-generation thermoelectrics

    Materials Today Energy · 2024-09-30

    Kıvanç Sağlık, Siddharth Srinivasan, Varsha Victor, Xizu Wang, Wei Zhang, Qingyu Yan

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  6. High efficiency thermal insulation material based on boron hybrid silicone rubber and hollow microspheres

    Journal of Materials Science · 2025-01-01

    Yulong Zhang, Wei Liu, Yan-zhen Xu, Siyu Wang, Han-yue Zheng, Jilong Li, Qiang Zhou

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  7. Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network

    Advanced Science · 2025-02-07

    Yang Wang, Changliang Zhu, Shuzhe Zhang, Changsheng Xiang, Zhibin Gao, Guimei Zhu, Jun Feng Sun, Xiangdong Ding, Baowen Li, Xiangying Shen

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  8. Machine Learning-Directed Discovery of Ultrahigh Young’s Modulus Crystals

    The Journal of Physical Chemistry C · 2025-05-23

    Bo Zhu, Qian Shao

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  9. Machine learning prediction of HSE06-level band gaps in two-dimensional semiconductors with reference-guided graph neural networks

    Chinese Physics B · 2025-08-26

    Zhen 振 Wan 万, Shun-Bo 顺波 Jiang 姜, Yuan 圆 Li 李, Hui 辉 Wang 王, Zong‐Liang Li, Guang‐Ping Zhang

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  10. Accelerating the discovery of materials with expected thermal conductivity via a synergistic strategy of DFT and interpretable deep learning

    Materials Futures · 2025-09-18

    Yuxuan Zeng, Wei Cao, Yijing Zuo, Tan Peng, Yue Hou, Ling Miao, Ziyu Wang, Jing Shi

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  11. Machine Learning for Thermal Transport Prediction in Nanoporous Materials: Progress, Challenges, and Opportunities

    Nanomaterials · 2025-10-31

    Amirehsan Ghasemi, Murat Barışık

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  12. Reducing lattice thermal conductivity via phonon engineering: Strategies for high-performance thermoelectrics

    Chinese Physics B · 2025-11-06

    Yayu 亚雨 Wang 王, Jue Hou, Ming Yang, Xingli Zhang

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  13. Machine learning–driven thermoelectric materials: Review on prediction, optimization, and discovery

    Journal of Alloys and Compounds · 2025-12-20

    Xinmei Zhang, Xingxing Wang, Wei Wang, Zhipeng Yuan, Jin Zhang Peng, Jianjun Shi, Peng He, Yunfeng Chang

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  14. Thermal conductivity of carbon nanotubes using nonequilibrium molecular dynamics combined with a machine learning potential

    Chinese Physics B · 2026-01-07

    Jia-Hua Liu, Shuo Cui, Feng Guo, Yu-Shi Wen, Chun-Liang Ji, Xiao-Chun Wang

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  15. Artificial intelligence and the frontier of phonon engineering: a perspective on discovering extreme thermal materials

    Journal of Materials Science Materials Theory · 2026-07-16

    Ming Hu

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  16. Studying Heat Conduction Properties by Using Machine Learning Potentials: a Brief Review

    Chinese Physics Letters · 2026-08-01

    Yangjun 杨军 Qin 秦, Zhicheng Zong, Tianhao 天豪 Li 李, Haisheng Fang, Guimei 桂妹 Zhu 朱, Jinwu Jiang, Nuo Yang

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  17. Artificial Intelligence-Enabled Cross-Scale Thermal Design: A Machine-Learning-Centered Review from Atoms to Devices

    Acta Physica Sinica · 2026-09-04

    Qikun Tian, Enbo Zhang, JIANG Yuting, Z. Z. Qin, Wang Huimin, Qin Guangzhao

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