Driving Thermoelectric Optimization in AgSbTe2 via Design of Experiments and Machine Learning
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913599569526784 |
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| author | Pöhls, Jan-Hendrik Lo, Chun-Wan Timothy MacIver, Marissa Tseng, Yu-Chih Mozharivskyj, Yurij |
| author_facet | Pöhls, Jan-Hendrik Lo, Chun-Wan Timothy MacIver, Marissa Tseng, Yu-Chih Mozharivskyj, Yurij |
| contents | Systemic optimization of thermoelectric materials is arduous due to their conflicting electrical and thermal properties. A strategy based on Design of Experiments and machine learning is developed to optimize the thermoelectric efficiency of AgSb1+xTe2+y, an established thermoelectric. From eight experiments, high thermoelectric performance in AgSb1.021Te2.04 is revealed with a peak and average thermoelectric figure of merit of 1.61 +/- 0.24 at 600 K and 1.18 +/- 0.18 (300 - 623 K), respectively, which is over 30% higher than the best literature values for AgSb1+xTe2+y. Ag-deficiency and suppression of secondary phases in AgSb1.021Te2.04 improves the electrical properties and reduces the thermal conductivity (~0.4 W m-1 K-1). Our strategy is implemented into an open-source graphical user interface, and it can be used to optimize the methodologies, properties, and processes across different scientific fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04699 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Driving Thermoelectric Optimization in AgSbTe2 via Design of Experiments and Machine Learning Pöhls, Jan-Hendrik Lo, Chun-Wan Timothy MacIver, Marissa Tseng, Yu-Chih Mozharivskyj, Yurij Materials Science Systemic optimization of thermoelectric materials is arduous due to their conflicting electrical and thermal properties. A strategy based on Design of Experiments and machine learning is developed to optimize the thermoelectric efficiency of AgSb1+xTe2+y, an established thermoelectric. From eight experiments, high thermoelectric performance in AgSb1.021Te2.04 is revealed with a peak and average thermoelectric figure of merit of 1.61 +/- 0.24 at 600 K and 1.18 +/- 0.18 (300 - 623 K), respectively, which is over 30% higher than the best literature values for AgSb1+xTe2+y. Ag-deficiency and suppression of secondary phases in AgSb1.021Te2.04 improves the electrical properties and reduces the thermal conductivity (~0.4 W m-1 K-1). Our strategy is implemented into an open-source graphical user interface, and it can be used to optimize the methodologies, properties, and processes across different scientific fields. |
| title | Driving Thermoelectric Optimization in AgSbTe2 via Design of Experiments and Machine Learning |
| topic | Materials Science |
| url | https://arxiv.org/abs/2412.04699 |