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Machine learning for next-generation thermoelectrics
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AI
DOI：10.1016/j.mtener.2024.101700.png)
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摘要
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Thermoelectricity offers a ground-breaking solution for capturing waste heat and transforming it into valuable electricity. Despite its promise, the quest for high-performance materials faces challenges due to costly and time-intensive experimental processes. This review investigates the transformative role of machine learning in accelerating material discovery and optimization in thermoelectric research. Various machine learning algorithms employed in this domain are examined, alongside advancements in predicting lattice thermal conductivity and optimizing electronic properties such as power factor, Seebeck coefficient, and bandgap. Additionally, the manuscript explores machine learning applications in device optimization to enhance efficiency and power output. In conclusion, the review outlines the current challenges and prospects of machine learning in thermoelectric research. A comprehensive analysis of machine learning's impact on diverse thermoelectric properties promises to streamline material identification and refinement processes, paving the way for efficient energy conversion. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Keyword:
Thermoelectricity
Thermal conductivity
Thermoelectric devices
Prediction
期刊
IF:
8.6
论文数:
2.4K
被引数:
1.2W
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Segmented thermoelectric generator modelling and optimization using artificial neural networks by iterative training.png)
ENERGY AND AI
OAAI
ENERGY AND AIIF9.6
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