Efficient Optimization of the Performance of Mn2+-Doped Kesterite Solar Cell: Machine Learning Aided Synthesis of High Efficient Cu2(Mn,Zn)Sn(S,Se)4 Solar Cells
Efficient Optimization of the Performance of Mn2+-Doped Kesterite Solar Cell: Machine Learning Aided Synthesis of High Efficient Cu2(Mn,Zn)Sn(S,Se)4 Solar Cells
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Mn2+掺杂黄锡矿太阳能电池性能的高效优化：机器学习辅助合成高效Cu2(Mn,Zn)Sn(S,Se)4太阳能电池
DOI：
10.1002/solr.201800198
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发表时间：
2018-12-01
期刊：
影响因子：
7.9
通讯作者：
Zhang, Yi
中科院分区：
文献类型：
--
作者：
Li, Xiuling;Hou, Zhufeng;Zhang, Yi
等电子阳离子取代是降低CZTSSe中Cu-Zn反位缺陷密度的潜在方法，从而提高CZTSSe太阳能电池的V-OC和性能。传统上，适当的掺杂浓度是通过反复试验的方法来确定的，这会花费大量的时间和材料。如何缩短找到合适掺杂浓度的时间是太阳能电池发展的一大挑战。在这里，作者利用机器学习模型进行了自适应设计，预测 CZTSSe 太阳能电池中 Mn2+ 离子的最佳掺杂比例，以提高太阳能电池效率。借助机器学习预测，作者快速高效地找到了CZTSSe太阳能电池中Mn2+的最佳掺杂比例为0.05，实验中获得了最高8.9%的太阳能电池效率。 Mn掺杂CZTSSe的进一步实验表征表明，Mn掺杂后CZTSSe中的缺陷由反位Cu-Zn缺陷转变为V-Cu缺陷。我们的研究结果表明，机器学习是一种非常强大且有效的方法，可以帮助开发太阳能电池材料及其在光伏领域的应用。
Isoelectronic cation substitution is a potential method to decrease the density of Cu-Zn anti-site defects in CZTSSe, thus improving the V-OC and performance of CZTSSe solar cells. The proper doping concentration is determined traditionally by the trial and error approach, costing much time, and materials. How to shorten the time to find the proper doping concentration is a big challenge for the development of solar cells. Here, by utilizing the machine learning model, the authors carry out an adaptive design for predicting the optimal doping ratio of Mn2+ ions in CZTSSe solar cells for improved solar cell efficiency. With the help of machine learning prediction, the authors rapidly and efficiently find the optimal doping ratio of Mn2+ in CZTSSe solar cells to be 0.05, achieving a highest solar cell efficiency of 8.9% in experiment. Further experimental characterizations of Mn-doped CZTSSe show that the defect in CZTSSe after Mn doping is changed from an anti-site Cu-Zn defect to V-Cu defect. Our findings suggest that machine learning is a very powerful and efficient approach to aid the development of solar cell materials for its application in the photovoltaic field.