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Efficient Optimization of the Performance of Mn 2+ ‐Doped Kesterite Solar Cell: Machine Learning Aided Synthesis of High Efficient Cu 2 (Mn,Zn)Sn(S,Se) 4 Solar Cells
Author(s) -
Li Xiuling,
Hou Zhufeng,
Gao Shoushuai,
Zeng Yu,
Ao Jianping,
Zhou Zhiqiang,
Da Bo,
Liu Wei,
Sun Yun,
Zhang Yi
Publication year - 2018
Publication title -
solar rrl
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.544
H-Index - 37
ISSN - 2367-198X
DOI - 10.1002/solr.201800198
Subject(s) - doping , kesterite , solar cell , materials science , photovoltaic system , solar cell efficiency , optoelectronics , nanotechnology , artificial intelligence , computer science , electrical engineering , czts , engineering
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 Mn 2+ 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 Mn 2+ 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.