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Applications of Machine Learning in Thermoelectric Materials
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Ye SHENG1, Jinyan NING1, Jiong YANG1, 2
Journal of the Chinese Ceramic Society | 2023, 51(2) : 499 - 509
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Journal of the Chinese Ceramic Society | 2023, 51(2): 499-509
• Review •
Applications of Machine Learning in Thermoelectric Materials
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Ye SHENG1, Jinyan NING1, Jiong YANG1, 2
Affiliations
  • 1.Materials Genome Institute, Shanghai University, Shanghai 200444, China
  • 2.Zhejiang Laboratory, Hangzhou 311100, China

    • SHENG Ye (1994-), female, Doctoral candidate. E-mail:

About Author:

SHENG Ye (1994-), female, Doctoral candidate. E-mail:

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220863
Outline
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Thermoelectric materials are environmental-friendly energy conversion materials. Their performance optimization is a complex issue of multi-parameter coordination, which becomes a challenge. Although the computational simulation and experimental methods for thermoelectric materials have developed rapidly, the efficiency of searching thermoelectric materials still needs to be further improved. Machine learning has some advantages of low computational cost and high prediction speed, which can shorten the search process and accelerate the corresponding studies on the structure and performance optimization of thermoelectric materials. This review introduced the research progress on machine learning for small sample numerical data (data volume is about 102), large sample numerical data (data volume >104) and image data in thermoelectric materials from the perspective of data types. Moreover, different machine learning algorithm models used for the structure and performance of thermoelectric materials in different data types were discussed. In addition, the future development and application direction were also prospected.

thermoelectric materials  /  machine learning  /  graph convolutional neural networks
盛晔, 宁金妍, 杨炯. 机器学习在热电材料领域中的应用. 硅酸盐学报, 2023 , 51 (2) : 499 -509 . DOI: 10.14062/j.issn.0454-5648.20220863
Ye SHENG, Jinyan NING, Jiong YANG. Applications of Machine Learning in Thermoelectric Materials[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 499 -509 . DOI: 10.14062/j.issn.0454-5648.20220863
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Year 2023 volume 51 Issue 2
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Article Info
doi: 10.14062/j.issn.0454-5648.20220863
  • Receive Date：2022-10-12
  • Online Date：2026-10-20
  • Published：2023-02-20
Article Data
Affiliations
History
  • Received：2022-10-12
  • Revised：2022-11-05
Funding
Affiliations
    1.Materials Genome Institute, Shanghai University, Shanghai 200444, China
    2.Zhejiang Laboratory, Hangzhou 311100, China

Corresponding:

YANG Jiong (1981-), male, Ph.D., Professor. E-mail:
NING Jinyan (1985-), female, Ph.D., Experimentalist.
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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