Identifying Optimal Strain in Bismuth Telluride Thermoelectric Film by Combinatorial Gradient Thermal Annealing and Machine Learning,ACS Combinatorial Science

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Identifying Optimal Strain in Bismuth Telluride Thermoelectric Film by Combinatorial Gradient Thermal Annealing and Machine Learning
ACS Combinatorial Science Pub Date : 2020-11-04 , DOI: 10.1021/acscombsci.0c00112
Michiko Sasaki ₁ , Shenghong Ju _{2,

3,

4} , Yibin Xu ₅ , Junichiro Shiomi _{2,

5} , Masahiro Goto ₁

Affiliation

The thermoelectric properties of bismuth telluride thin film (BTTF) was tuned by inducing internal strain through a combination of combinatorial gradient thermal annealing (COGTAN) and machine learning. BTTFs were synthesized via magnetron sputter coating and then treated by COGTAN. The crystal structure and thermoelectric properties, namely Seebeck coefficient and thermal conductivity, of the treated samples were analyzed via micropoint X-ray diffraction and scanning thermal probe microimaging, respectively. The obtained combinatorial data reveals the correlation between internal strain and the thermoelectric properties. The Seebeck coefficient of BTTF exhibits largest sensitivity, where the value ranges from 7.9 to −108 μV/K. To further explore the possibility to enhance Seebeck coefficient, the combinatorial data were subjected to machine learning. The trained model predicts that optimal strains of 3–4% and 1–2% along the a- and c-axis, respectively, significantly improve Seebeck coefficient. The technique demonstrated herein can be used to predict and enhance the performance of thermoelectric materials by inducing internal strain.

中文翻译：

通过组合梯度热退火和机器学习识别碲化铋热电膜中的最佳应变

碲化铋薄膜 (BTTF) 的热电特性是通过组合梯度热退火 (COGTAN) 和机器学习的组合诱导内部应变来调节的。BTTFs 是通过磁控溅射镀膜合成的，然后用 COGTAN 处理。分别通过微点X射线衍射和扫描热探针显微成像分析了处理样品的晶体结构和热电性能，即塞贝克系数和热导率。获得的组合数据揭示了内部应变与热电特性之间的相关性。BTTF 的塞贝克系数表现出最大的灵敏度，其值范围为 7.9 至 -108 μV/K。为了进一步探索提高塞贝克系数的可能性，组合数据经过机器学习。训练好的模型预测沿线的最佳应变为 3-4% 和 1-2%a - 和c -轴分别显着提高了塞贝克系数。本文演示的技术可用于通过诱导内部应变来预测和增强热电材料的性能。

更新日期：2020-12-14

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