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Machine learning algorithms can enhance the design and experimental processing of solar cells, resulting in increased conversion efficiency. In this study, we introduce a novel machine learning-based methodology for optimizing the Pareto front of four-terminal (4T) perovskite-copper indium selenide (CIS) tandem solar cells (TSCs). By training a neural network using the Bayesian regularization-backpropagation algorithm via Hammersley sampling, we achieve high prediction accuracy when testing with unseen data through random sampling. This surrogate model not only reduces computational costs but also potentially enhances device performance, increasing from 29.4% to 30.4% while simultaneously reducing material costs for fabrication by 50%. Comparing experimentally fabricated cells with the predicted optimal cells, the latter show a thinner front contact electrode, charge-carrier transport layer, and back contact electrode. Highly efficient perovskite cells identified from the Pareto front have a perovskite layer thickness ranging from 420 to 580 nm. Further analysis reveals the front contact electrode needs to be thin, while the back contact electrode can have a thickness ranging from 100 to 145 nm and still achieve high efficiency. The charge-carrier transport layers play a crucial role in minimizing interface recombination and ensuring unidirectional current flow. The optimal design space suggests thinner electron and hole transport layer thicknesses of 7 nm, down from 23 to 10 nm, respectively. It indicates a balanced charge-carrier extraction is crucial for an optimized perovskite cell. Overall, the presented methodology and optimized design parameters have the potential to enhance the performance of 4T perovskite/CIS TSC while reducing material fabrication costs. [ABSTRACT FROM AUTHOR]

Through the analysis of scales on the drift–diffusion device model of a planar-structured perovskite solar cell, we have obtained accurate-analytical expressions that capture the recombination losses within the cell. The recombination losses are resolved into the radiative and Shockley–Read–Hall mechanisms, as well as interfacial recombination between the perovskite and electron/hole-transporting layers. After parameter calibration with the state of the art planar perovskite solar cell of 23.5% efficiency, the percentage contribution of various recombination loss channels within a planar-structured perovskite solar cell is analytically determined through derived scales and numerically verified at the condition of an open-circuit voltage and a short-circuit current, as well as gives a good prediction of a dominant recombination mechanism within the cell. On this basis, a comparison of loss analysis between the estimated scales and numeric results is carried out at the condition of an open-circuit voltage when a wide range of parameters influencing the recombination current is deviated simultaneously, and a good agreement is obtained. [ABSTRACT FROM AUTHOR]

Meso-structured perovskite solar cells (PSC), utilizing a mesoporous absorber layer consisting of mesoporous metal oxide and the perovskite material inside, are still delivering the highest solar cell efficiency for perovskite-based solar cells up to date. Their outstanding performance critically depends on the nanoscopic morphology formed inside the mesoporous absorber layer. This, however, is not accounted for in most of the perovskite device models, as they are based on an effective-medium formulation for the mesoporous absorber layer, and the details of its underlying morphology are ignored. The mesoporous absorber layer is treated as a two-phase model that describes intrinsic solar cell physics such as free charge carrier generation, carrier transport, and recombination within the two phases, as well as at the interface between the two phases. We derive a spatially smoothed device model for meso-structured PSCs based on volume-averaging of electric potential and electron and hole concentrations of the two-phase model, and this spatially smoothed formulation captures two essential morphological descriptors that are not found in existing effective-medium formulations for meso-structured PSCs, namely, surface-to-volume ratio and porosity inside the mesoporous layer. Furthermore, we determine the explicit functional forms of the effective parameters in the spatially smoothed model for the case of an ideal "Spaghetti" blend morphology. [ABSTRACT FROM AUTHOR]