| 研究課題/領域番号 |
19K14902
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| 研究機関 | 東京大学 |
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研究代表者 |
鞠 生宏 東京大学, 大学院工学系研究科(工学部), 客員研究員 (30809645)
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| 研究期間 (年度) |
2019-04-01 – 2022-03-31
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| キーワード | マテリアルズ・インフォマティクス / 伝熱機能材料 |
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| 研究実績の概要 |
This mateirals inforamtics related research project went smoothly in 2020. We have successfully applied machine learning to design materials for improving the thermoelectric property and high-throughput screening for high thermal conductivity crystals.
The main achievements are summarized as follows:
1. We identified the optimal strain in bismuth telluride thermoelectric film by combinatorial gradient thermal annealing and machine learning.
2. The high-throughput screening and transfer learning has been successfully applied to screen high thermal conductivity crystals in Materials Project database.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
This materials informatics related research project goes smoothly as expected in the proposal. Up to present, we have applied the materials informatics method to design thermal conduction, thermal radiation, magnetic and thermoelectric properties. Our work has shown great advantage in designing materials or structures with optimal transport properties for different energy carriers including phonons, electrons, photons and magnons. The mentioned research works were published in international journals or presented on conferences.
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| 今後の研究の推進方策 |
Following the research proposal, we will push forward the work related with:
(1) explore anisotropic thermal conductivity crystals via high-throughput screening, and (2) continue to design novel functional materials using various materials informatics method including the Bayesian optimization, the Monte Carlo tree search, and the quantum annealing.
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| 次年度使用額が生じた理由 |
Due to the coronavirus pandemic situation, we hope to be able to use the unfinished funding in the next fiscal year.
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