中国物理B ›› 2023, Vol. 32 ›› Issue (4): 46301-046301.doi: 10.1088/1674-1056/acbaf4
所属专题： SPECIAL TOPIC — Smart design of materials and design of smart materials
Prediction of lattice thermal conductivity with two-stage interpretable machine learning
Jinlong Hu(胡锦龙)1,†, Yuting Zuo(左钰婷)1,†, Yuzhou Hao(郝昱州)1,†, Guoyu Shu(舒国钰)1, Yang Wang(王洋)1, Minxuan Feng(冯敏轩)1, Xuejie Li(李雪洁)1, Xiaoying Wang(王晓莹)1, Jun Sun(孙军)1, Xiangdong Ding(丁向东)1, Zhibin Gao(高志斌)1,‡, Guimei Zhu(朱桂妹)2,§, and Baowen Li(李保文)3,4,5
- 1 State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China;
2 School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China;
3 Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China;
4 Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China;
5 Paul M. Rady Department of Mechanical Engineering and Department of Physics, University of Colorado, Boulder, Colorado 80305-0427, USA
Prediction of lattice thermal conductivity with two-stage interpretable machine learning
Jinlong Hu(胡锦龙)1,†, Yuting Zuo(左钰婷)1,†, Yuzhou Hao(郝昱州)1,†, Guoyu Shu(舒国钰)1, Yang Wang(王洋)1, Minxuan Feng(冯敏轩)1, Xuejie Li(李雪洁)1, Xiaoying Wang(王晓莹)1, Jun Sun(孙军)1, Xiangdong Ding(丁向东)1, Zhibin Gao(高志斌)1,‡, Guimei Zhu(朱桂妹)2,§, and Baowen Li(李保文)3,4,5
- 1 State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China;
2 School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China;
3 Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China;
4 Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China;
5 Paul M. Rady Department of Mechanical Engineering and Department of Physics, University of Colorado, Boulder, Colorado 80305-0427, USA
摘要： Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network (CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model-sure independence screening and sparsifying operator (SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database (OQMD) (https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultra-low lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.
中图分类号: (Phonons in crystal lattices)
- 63.20.-e