(185a) Optimization of High-Performance Perovskite Solar Cells Via Algorithm-Guided Experimentation

Perovskite solar cells (PSCs) have great potential for clean power generation. However, despite recent advancements [1–4], research that focuses on high-performance PSC optimization remains challenging due to the large number of processing variables across multiple fabrication steps that require systematic optimization. Moreover, the strong interdependence among design variables such as fabrication processing variables and material composition complicates the device engineering and search for optimal designs.

In this work, we present an algorithm-guided experimentation approach for systematically improving the photovoltaic performance of metal halide perovskite-based solar cells. We employ black-box optimization algorithms [5,6], such as Stable Noisy Optimization by Branch and Fit (SNOBFIT) [7] and Branch-and-Model (BAM) [8], which guide the sampling of the design space to search for optimal designs. We then fabricate devices suggested by the algorithms and test them under illumination to validate and refine the optimization process.

We report notable enhancements in power conversion efficiency achieved by systematically tuning the processing conditions throughout the device stack, including the perovskite, electron transport, and hole transport layers, without altering any chemical ingredients or device configuration. We further investigate the identified novel designs using characterization techniques such as grazing incidence wide angle X-ray scattering and X-ray photoelectron spectroscopy. Our approach demonstrates an effective combination of mathematical optimization and experimental research, providing an effective tool for research on emerging materials like halide perovskites and 2D materials in which the systematic optimization of complex and interrelated design variables remains relatively underexplored.

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