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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.11568 |
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| _version_ | 1866916770139340800 |
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| author | Li, Jielan Chen, Zekun Wang, Qian Yang, Han Lu, Ziheng Li, Guanzhi Chen, Shuizhou Zhu, Yu Liu, Xixian Tan, Junfu Tang, Mingfa Zhou, Yichi Zeni, Claudio Fowler, Andrew Zügner, Daniel Pinsler, Robert Horton, Matthew Xie, Tian Liu, Tie-Yan Liu, Haiguang Qin, Tao Lv, Bing Donadio, Davide Hao, Hongxia |
| author_facet | Li, Jielan Chen, Zekun Wang, Qian Yang, Han Lu, Ziheng Li, Guanzhi Chen, Shuizhou Zhu, Yu Liu, Xixian Tan, Junfu Tang, Mingfa Zhou, Yichi Zeni, Claudio Fowler, Andrew Zügner, Daniel Pinsler, Robert Horton, Matthew Xie, Tian Liu, Tie-Yan Liu, Haiguang Qin, Tao Lv, Bing Donadio, Davide Hao, Hongxia |
| contents | Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains unknown. Using deep learning accelerated crystal structure prediction and first-principles calculation, we systematically explore the thermal conductivity landscape of inorganic crystals. We brute-force over half a million ordered crystalline structures, encompassing an extensive coverage of local energy minima in binary compounds with up to four atoms per primitive cell. We confirm diamond sets the upper bound of thermal conductivity within our search space, very likely also among all stable crystalline solids at ambient conditions. We also identify over 20 novel crystals surpassing silicon in thermal conductivity, validated by density functional theory. These include a semiconductor TaN with ultrahigh thermal conductivity (~900 $\mathrm{W\cdot m^{-1}\cdot K^{-1}}$), and metallic compounds such as MnV that exhibit high lattice and electronic thermal conductivity simultaneously, a distinctive feature not observed before. These results as well as the deep learning-driven screening method, redefine the landscape of thermal transport and establish a large open-access database for future materials discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11568 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning Li, Jielan Chen, Zekun Wang, Qian Yang, Han Lu, Ziheng Li, Guanzhi Chen, Shuizhou Zhu, Yu Liu, Xixian Tan, Junfu Tang, Mingfa Zhou, Yichi Zeni, Claudio Fowler, Andrew Zügner, Daniel Pinsler, Robert Horton, Matthew Xie, Tian Liu, Tie-Yan Liu, Haiguang Qin, Tao Lv, Bing Donadio, Davide Hao, Hongxia Materials Science Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains unknown. Using deep learning accelerated crystal structure prediction and first-principles calculation, we systematically explore the thermal conductivity landscape of inorganic crystals. We brute-force over half a million ordered crystalline structures, encompassing an extensive coverage of local energy minima in binary compounds with up to four atoms per primitive cell. We confirm diamond sets the upper bound of thermal conductivity within our search space, very likely also among all stable crystalline solids at ambient conditions. We also identify over 20 novel crystals surpassing silicon in thermal conductivity, validated by density functional theory. These include a semiconductor TaN with ultrahigh thermal conductivity (~900 $\mathrm{W\cdot m^{-1}\cdot K^{-1}}$), and metallic compounds such as MnV that exhibit high lattice and electronic thermal conductivity simultaneously, a distinctive feature not observed before. These results as well as the deep learning-driven screening method, redefine the landscape of thermal transport and establish a large open-access database for future materials discovery. |
| title | Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning |
| topic | Materials Science |
| url | https://arxiv.org/abs/2503.11568 |