Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

Solar cells based on quaternary kesterite compounds like Cu₂ZnGeSe₄ 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 Cu₂ZnGeSe₄ 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 Cu₂ZnGeSe₄ 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 Cu₂ZnGeSe₄ 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 Cu₂ZnGeSe₄ 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.