DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells

Key Points

• The aim is to develop a framework that accurately predicts the properties of defects in CdTe-based solar cells.
• Utilized machine learning techniques to create a force field framework.
• Modeled native defects, extrinsic dopants, and impurities in materials for solar cells.
• Analyzed configurations in Cd/Zn–Te/Se/S compounds.
• Successfully predicted energies of various defect configurations.
• Demonstrated improved accuracy in modeling defects compared to traditional methods.
• Identified critical defects impacting the performance of CdTe-based solar cells.

Abstract