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....

中文翻译：

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直接确定大晶格失配的CdS / CdTe异质结构的界面结构和能带偏移

CdS / CdTe异质结在确定CdTe太阳能电池的能量转换效率中起着重要作用。但是，由于CdS和CdTe之间的晶格失配较大，界面结构仍是未知的，这对理解其界面效应提出了巨大的挑战。通过将基于神经网络的机器学习方法与基于随机表面行走的全局优化方法相结合，我们首先证明了CdSTe系统的鲁棒性和可靠性，为其提供了神经网络潜力。基于上述潜力，我们然后使用模拟退火以获得CdS / CdTe界面的最佳结构。我们发现最稳定的结构同时具有体积和紊乱的特征。使用获得的结构，我们通过一次成像的第一性原理计算，通过将异质结构中的核心能级与主体中的核心能级对齐来直接计算CdS和CdTe之间的带隙。我们计算出的带偏移为0 ....