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摘要:
The CdS/CdTe heterojunction plays an important role in determining the energy conversion efficiency of CdTe solar cells.However,the interface structure remains unknown,due to the large lattice mismatch between CdS and CdTe,posing great challenges to achieving an understanding of its interfacial effects.By combining a neural-network-based machine-learning method and the stochastic surface walking-based global optimization method,we first train a neural network potential for CdSTe systems with demonstrated robustness and reliability.Based on the above potential,we then use simulated annealing to obtain the optimal structure of the CdS/CdTe interface.We find that the most stable structure has the features of both bulks and disorders.Using the obtained structure,we directly calculate the band offset between CdS and CdTe by aligning the core levels in the heterostructure with those in the bulks,using one-shot first-principles calculations.Our calculated band offset is 0.55 eV,in comparison with 0.70 eV,obtained using other indirect methods.The obtained interface structure should prove useful for further study of the properties of CdTe/CdS heterostructures.Our work also presents an example which is applicable to other complex interfaces.
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篇名 Directly Determining the Interface Structure and Band Offset of a Large-Lattice-Mismatched CdS/CdTe Heterostructure
来源期刊 中国物理快报(英文版) 学科
关键词
年,卷(期) 2020,(9) 所属期刊栏目 CONDENSED MATTER:STRUCTURE,MECHANICAL AND THERMAL PROPERTIES
研究方向 页码范围 46-51
页数 6页 分类号
字数 语种 英文
DOI 10.1088/0256-307X/37/9/096802
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中国物理快报(英文版)
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0256-307X
11-1959/O4
16开
北京中关村中国科学院物理研究所内
1984
eng
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