在CdSexTe1-x太阳能电池中加速非adiabatic分子动力学模拟,具有反复的神经网络
Zhaosheng Zhang1, Yanbo Liu1, Qing Xiong1
1College of Chemistry and Materials Science, Hebei University, Baoding 071002, People's Republic of China.
The Journal of chemical physics
|July 10, 2025
概括
在 Telluride (CdTe) 薄膜太阳能电池中的兴奋剂通过减少非辐射重组来提高效率. 这项研究揭示了Se的兴奋剂.
科学领域:
- 材料科学 材料科学 材料科学
- 固态物理 固态物理
- 计算化学计算化学
背景情况:
- Telluride (CdTe) 是薄膜太阳能电池的一个关键材料.
- (Se) 兴奋剂对于提高CdTe太阳能电池效率至关重要.
- 兴奋剂效应的微观机制尚不清楚.
研究的目的:
- 阐明在CdTe中的非辐射电子孔再组合中Se兴奋剂的微观机制.
- 调查Se对重组寿命和缺陷水平的影响.
- 开发一个机器学习框架,以加速非adiabatic (NA) 合计算.
主要方法:
- 密度函数理论 (DFT) 用于电子结构计算.
- 非adiabatic分子动力学 (NA MD) 模拟.
- 经常性神经网络 (RNN) 模型 (LSTM,GRU,ConvLSTM) 用于NA合预测.
- 本质缺陷计算. 本质缺陷计算.
主要成果:
- doping 减少了非adiabatic 合,并加快了声子诱导的脱凝.
- 非辐射重组寿命通过Se兴奋剂被延长.
- ConvLSTM模型在预测非辐射行为方面表现出很高的准确性.
- 降低了Cd空位 (VCd) 和Te空位 (VTe) 的过渡能量水平.
- 精神兴奋剂抑制了深层陷的形成.
结论:
- 化剂提供了一个多层次的机制来调节CdTe太阳能电池中的非辐射重组途径.
- 开发的机器学习框架,特别是ConvLSTM,有效地加速了光电子材料的NA模拟.
- 这项工作提供了通过控制的Se结合优化CdTe太阳能电池性能的见解.
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