热导率
格子(音乐)
热电效应
材料科学
热电材料
计算机科学
机器学习
可解释性
人工智能
热传导
卷积神经网络
凝聚态物理
热力学
物理
复合材料
声学
作者
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
出处
期刊:Chinese Physics B
[IOP Publishing]
日期:2023-02-10
卷期号:32 (4): 046301-046301
被引量:5
标识
DOI:10.1088/1674-1056/acbaf4
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.
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