High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction
Thermoelectric materials, which convert thermal energy directly into electricity, hold promise for sustainable energy applications. However, accurately predicting their efficiency, quantified by the figure of merit (zT), remains challenging, especially for doped materials. Here we present a machine...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127524009274 |
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| author | Yuelin Wang Chengquan Zhong Jingzi Zhang Honghao Yao Junjie Chen Xi Lin |
| author_facet | Yuelin Wang Chengquan Zhong Jingzi Zhang Honghao Yao Junjie Chen Xi Lin |
| author_sort | Yuelin Wang |
| collection | DOAJ |
| description | Thermoelectric materials, which convert thermal energy directly into electricity, hold promise for sustainable energy applications. However, accurately predicting their efficiency, quantified by the figure of merit (zT), remains challenging, especially for doped materials. Here we present a machine learning (ML) approach, the stacking model, that significantly improves zT prediction accuracy for doped thermoelectric. By combining five regression models through stacking ensemble learning and introducing 100 coordination number features alongside conventional features, our model achieves a coefficient of determination (R2) value of 0.970. This high performance demonstrates unprecedented sensitivity to zT variations due to doping. We validate our model using an expanded dataset of over 230 new materials from recent literature. The model identifies 43 potential high-zT materials, including Pb0.97K0.03Te0.65S0.25Se0.1 with a predicted zT of 1.9. Density functional theory calculations confirm the superior electrical properties of this compound. Our approach offers an efficient strategy for large-scale screening of high-performance thermoelectric materials, potentially accelerating their discovery and development for energy applications. |
| format | Article |
| id | doaj-art-7ae81134613d40ff871fdfb40afb7390 |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-7ae81134613d40ff871fdfb40afb73902025-01-09T06:12:23ZengElsevierMaterials & Design0264-12752025-01-01249113552High-Performance stacking ensemble learning for thermoelectric figure-of-merit predictionYuelin Wang0Chengquan Zhong1Jingzi Zhang2Honghao Yao3Junjie Chen4Xi Lin5School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen 518055, Guangdong, ChinaSchool of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen 518055, Guangdong, ChinaSchool of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; Corresponding authors at: School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China.School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; Corresponding authors at: School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China.School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; Corresponding author.School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China; Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China; Corresponding authors at: School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China.Thermoelectric materials, which convert thermal energy directly into electricity, hold promise for sustainable energy applications. However, accurately predicting their efficiency, quantified by the figure of merit (zT), remains challenging, especially for doped materials. Here we present a machine learning (ML) approach, the stacking model, that significantly improves zT prediction accuracy for doped thermoelectric. By combining five regression models through stacking ensemble learning and introducing 100 coordination number features alongside conventional features, our model achieves a coefficient of determination (R2) value of 0.970. This high performance demonstrates unprecedented sensitivity to zT variations due to doping. We validate our model using an expanded dataset of over 230 new materials from recent literature. The model identifies 43 potential high-zT materials, including Pb0.97K0.03Te0.65S0.25Se0.1 with a predicted zT of 1.9. Density functional theory calculations confirm the superior electrical properties of this compound. Our approach offers an efficient strategy for large-scale screening of high-performance thermoelectric materials, potentially accelerating their discovery and development for energy applications.http://www.sciencedirect.com/science/article/pii/S0264127524009274Thermoelectric materialszTMachine LearningStacking ensemble model |
| spellingShingle | Yuelin Wang Chengquan Zhong Jingzi Zhang Honghao Yao Junjie Chen Xi Lin High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction Materials & Design Thermoelectric materials zT Machine Learning Stacking ensemble model |
| title | High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction |
| title_full | High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction |
| title_fullStr | High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction |
| title_full_unstemmed | High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction |
| title_short | High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction |
| title_sort | high performance stacking ensemble learning for thermoelectric figure of merit prediction |
| topic | Thermoelectric materials zT Machine Learning Stacking ensemble model |
| url | http://www.sciencedirect.com/science/article/pii/S0264127524009274 |
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