Multiscale Modeling of Quantum Dot Solar Cells: Integration of Density Functional Theory, SCAPS, Lambert W Analysis, and Machine Learning

Quantum dot solar cells (QDSCs) have attracted significant attention as next-generation photovoltaic technologies due to their tunable bandgaps, solution-processable fabrication, and potential for multiple-exciton generation. However, their practical implementation is still limited by suboptimal power conversion efficiency, defect-assisted recombination, voltage losses, interfacial instability, and the use of toxic heavy-metal quantum dots. In this study, we present a unified multiscale framework that integrates density functional theory (DFT), SCAPS-1D device simulation, Lambert W-based analytical modeling, and artificial intelligence (AI) to accelerate the optimization of QDSCs. DFT provides atomistic insights into electronic structure, defect states, and interfacial charge transfer, while SCAPS-1D enables device-level analysis of charge transport and recombination mechanisms. The Lambert W formalism allows explicit extraction of nonideal diode parameters, improving the interpretability of device characteristics. In parallel, machine learning and generative AI approaches enable rapid performance prediction, inverse design of optimized architectures, and efficient screening of lower-toxicity quantum dot materials. This integrated strategy bridges physics-based and data-driven methodologies, offering a scalable pathway toward enhancing the efficiency, stability, and environmental sustainability of QDSCs.