Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance. Issue 16 (19th April 2021)

• Record Type: Journal Article
• Authors: Grau-Luque, Enric; Anefnaf, Ikram; Benhaddou, Nada; Fonoll-Rubio, Robert; Becerril-Romero, Ignacio; Aazou, Safae; Saucedo, Edgardo; Sekkat, Zouheir; Perez-Rodriguez, Alejandro; Izquierdo-Roca, Victor; Guc, Maxim
• Is Part Of: Journal of materials chemistry. Volume 9:Issue 16(2021)
• Page Start: 10466
• Page End: 10476
• Publication Date: 2021-04-19
• Subjects: Materials -- Research -- Periodicals; Chemistry, Analytic -- Periodicals; Environmental sciences -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d1ta01299a
• Languages: English
• ISSNs: 2050-7488

Abstract

Abstract : This work provides insights for understanding and further developing the Cu2 ZnGeSe4 photovoltaic technology, and gives an example of the potential of combinatorial analysis and machine learning for the study of complex systems in materials research. Abstract : Solar cells based on quaternary kesterite compounds like Cu2 ZnGeSe4 are complex systems where the variation of one parameter can result in changes in the whole system, and, as consequence, in the global performance of the devices. In this way, analyses that take into account this complexity are necessary in order to overcome the existing limitations of this promising Earth-abundant photovoltaic technology. This study presents a combinatorial approach for the analysis of Cu2 ZnGeSe4 based solar cells. A compositional graded sample containing almost 200 solar cells with different [Zn]/[Ge] compositions is analyzed by means of X-ray fluorescence and Raman spectroscopy, and the results are correlated with the optoelectronic parameters of the different cells. The analysis results in a deep understanding of the stoichiometric limits and point defects formation in the Cu2 ZnGeSe4 compound, and shows the influence of these parameters on the performance of the devices. Then, intertwined connections between the compositional, vibrational and optoelectronic properties of the cells are revealed using a complex analytical approach. This is further extended using a machine learning algorithm. The latter confirms the correlation between the properties of the Cu2 ZnGeSe4 compound and the optoelectronic parameters, and also allows proposing a methodology for device performance prediction that is compatible with both research and industrial process monitoring environments. As such, this work not only provides valuable insights for understanding and further developing the Cu2 ZnGeSe4 photovoltaic technology, but also gives a practical example of the potential of combinatorial analysis and machine learning for the study of complex systems in materials research.