Combinatorial and machine learning approaches for the analysis of Cu<sub>2</sub>ZnGeSe<sub>4</sub>: influence of the off-stoichiometry on defect formation and solar cell performance
Enric Grau‐Luque, Ikram Anefnaf, Nada Benhaddou, Robert Fonoll‐Rubio, Ignacio Becerril‐Romero, Safae Aazou, Edgardo Saucedo, Zouheir Sekkat, A. Pérez-Rodrı́guez, Víctor Izquierdo‐Roca, Maxim Guc
Abstract
This work provides insights for understanding and further developing the Cu<sub>2</sub>ZnGeSe<sub>4</sub> photovoltaic technology, and gives an example of the potential of combinatorial analysis and machine learning for the study of complex systems in materials research.
Topics & Concepts
StoichiometryPhotovoltaic systemCombinatorial analysisWork (physics)Solar cellComputer scienceMaterials scienceEngineeringChemistryPhysical chemistryOptoelectronicsMathematicsMechanical engineeringElectrical engineeringCombinatoricsChalcogenide Semiconductor Thin FilmsQuantum Dots Synthesis And PropertiesMachine Learning in Materials Science