Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films
Abstract Studying the properties of thermoelectric materials needs substantial effort owing to the interplay of the trade-off relationships among the influential parameters. In view of this issue, artificial intelligence has recently been used to investigate and optimize thermoelectric materials. He...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | NPG Asia Materials |
| Online Access: | https://doi.org/10.1038/s41427-024-00536-w |
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| author | Takamitsu Ishiyama Koki Nozawa Takeshi Nishida Takashi Suemasu Kaoru Toko |
| author_facet | Takamitsu Ishiyama Koki Nozawa Takeshi Nishida Takashi Suemasu Kaoru Toko |
| author_sort | Takamitsu Ishiyama |
| collection | DOAJ |
| description | Abstract Studying the properties of thermoelectric materials needs substantial effort owing to the interplay of the trade-off relationships among the influential parameters. In view of this issue, artificial intelligence has recently been used to investigate and optimize thermoelectric materials. Here, we used Bayesian optimization to improve the thermoelectric properties of multicomponent III–V materials; this domain warrants comprehensive investigation due to the need to simultaneously control multiple parameters. We designated the figure of merit ZT as the objective function to improve and search for a five-dimensional space comprising the composition of InGaAsSb thin films, dopant concentration, and film-deposition temperatures. After six Bayesian optimization cycles, ZT exhibited an approximately threefold improvement compared to its values obtained in the random initial experimental trials. Additional analysis employing Gaussian process regression elucidated that a high In composition and low substrate temperature were particularly effective at increasing ZT. The optimal substrate temperature (205 °C) demonstrated the potential for depositing InGaAsSb thermoelectric thin films onto plastic substrates. These findings not only promote the development of thermoelectric devices based on III–V semiconductors but also highlight the effectiveness of using Bayesian optimization for multicomponent materials. |
| format | Article |
| id | doaj-art-b142dd6d1af746c1bc4b9473c3609864 |
| institution | Kabale University |
| issn | 1884-4057 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | NPG Asia Materials |
| spelling | doaj-art-b142dd6d1af746c1bc4b9473c36098642025-01-19T12:28:34ZengNature PortfolioNPG Asia Materials1884-40572024-03-011611710.1038/s41427-024-00536-wBayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin filmsTakamitsu Ishiyama0Koki Nozawa1Takeshi Nishida2Takashi Suemasu3Kaoru Toko4Institute of Applied Physics, University of TsukubaInstitute of Applied Physics, University of TsukubaGlobal Zero Emission Research Center, AISTInstitute of Applied Physics, University of TsukubaInstitute of Applied Physics, University of TsukubaAbstract Studying the properties of thermoelectric materials needs substantial effort owing to the interplay of the trade-off relationships among the influential parameters. In view of this issue, artificial intelligence has recently been used to investigate and optimize thermoelectric materials. Here, we used Bayesian optimization to improve the thermoelectric properties of multicomponent III–V materials; this domain warrants comprehensive investigation due to the need to simultaneously control multiple parameters. We designated the figure of merit ZT as the objective function to improve and search for a five-dimensional space comprising the composition of InGaAsSb thin films, dopant concentration, and film-deposition temperatures. After six Bayesian optimization cycles, ZT exhibited an approximately threefold improvement compared to its values obtained in the random initial experimental trials. Additional analysis employing Gaussian process regression elucidated that a high In composition and low substrate temperature were particularly effective at increasing ZT. The optimal substrate temperature (205 °C) demonstrated the potential for depositing InGaAsSb thermoelectric thin films onto plastic substrates. These findings not only promote the development of thermoelectric devices based on III–V semiconductors but also highlight the effectiveness of using Bayesian optimization for multicomponent materials.https://doi.org/10.1038/s41427-024-00536-w |
| spellingShingle | Takamitsu Ishiyama Koki Nozawa Takeshi Nishida Takashi Suemasu Kaoru Toko Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films NPG Asia Materials |
| title | Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films |
| title_full | Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films |
| title_fullStr | Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films |
| title_full_unstemmed | Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films |
| title_short | Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films |
| title_sort | bayesian optimization driven enhancement of the thermoelectric properties of polycrystalline iii v semiconductor thin films |
| url | https://doi.org/10.1038/s41427-024-00536-w |
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