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会议网站：http://mtt2024.csmnt.org.cn
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Dr. Shenghong Ju received his B.S. degree from Nanjing University of Aeronautics and Astronautics in 2008, and he obtained his Ph.D. degree in Engineering Thermophysics from Tsinghua University in 2014. He conducted postdoctoral research in Ecole Centrale Paris and the University of Tokyo from 2014 to 2019. He is currently an associate professor in Shanghai Jiao Tong University. His research mainly focuses on the nanoscale thermal transport and AI-assisted thermal functional materials design.Exploring materials with ultra-high intrinsic thermal conductivity has wide applications in the fields of thermal design of electronic devices. The traditional trial-and-error style is inefficient and with high cost. In this work, we introduce three successful cases by combining the machine learning and thermal transport calculations: (1) The exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning, (2) The active design of ternary B-C-N crystals with ultra-high thermal conductivity via particle swarm optimization, (3) The designing of high thermal conductivity polymer monomers based on the high-throughput molecular dynamics simulations and interpretable machine learning. Those successful cases have shown great advantage of designing materials with ultimate thermal property via materials informatics.（一）欢迎登录mTT2024官方网站：http://mtt2024.csmnt.org.cn 注册参会。
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【学会会员】张倩倩：18811456626
【会议通知】微纳热输运理论、材料与器件国际研讨会
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【mTT2024】参观考察
【mTT2024】邀请报告人——曹炳阳
【mTT2024】邀请报告人——Junsuk Rho
【mTT2024】邀请报告人——焦斌斌
【mTT2024】邀请报告人——马儒军
【mTT2024】邀请报告人——Pawel Keblinski
【mTT2024】邀请报告人——钱鑫
【mTT2024】邀请报告人——魏进家
