2024 AIChE Annual Meeting

(543e) Systematic Engineering of Perovskite Solar Cells through Algorithm-Guided Experimentation

Authors

Sanggyun Kim, Georgia Institute of Technology
Carlo Andrea Riccardo Perini, Georgia Institute of Technology
Juan-Pablo Correa-Baena, Georgia Institute of Technology
Nikolaos Sahinidis, Georgia Institute of Technology
Perovskite Solar Cells (PSCs) are a highly promising photovoltaic technology that has recently gained attention because of numerous advantages. These advantages include flexibility in material compositions [1, 2], outstanding optoelectronic properties [3, 4, 5], and low manufacturing costs [6]. However, despite significant improvements in recent years [7, 8, 9], research in the development of perovskite photovoltaics is still hindered by the lack of systematic methods for optimizing various design parameters that impact photovoltaic performance. For example, understanding the discrete impacts of material composition and fabrication processing parameters on device performance is challenging, and their intricate interconnectedness further complicates the discovery of optimal designs in a vast design space.

In this work, we present a method for systematic device optimization through a combined experimental-computational framework and report the improvements achieved in the performance of PSCs. In the experimental portion of our framework, we fabricate organic-inorganic hybrid metal halide PSCs through solution processing. We optimize several key design parameters, including the spin rate during perovskite layer deposition, the antisolvent quenching time, the dopant additive concentrations in the hole-selective spiro-OMeTAD layer, the temperature of the spiro-OMeTAD precursor solution, and the concentration and spray deposition rate of TiO2 used as the electron transport layer.

In the computational portion of our framework, we utilize black-box optimization techniques [10] to optimize multiple design parameters simultaneously and guide the search for optimal designs. To achieve this, we employ algorithms such as Stable Noisy Optimization by Branch and FIT (SNOBFIT) [11] and Branch-And-Model (BAM) [12]. These algorithms guide experimental design based on performance models that are trained and continuously improved with the data obtained by characterizing our fabricated devices under simulated solar illumination.

We present the results from our analysis of the identified optimal designs. Our analysis utilized advanced characterization techniques such as Grazing Incident Wide-Angle X-ray Scattering (GIWAXS) and X-ray Photoelectron Spectroscopy (XPS), to help us better understand the underlying scientific phenomena and key factors that contribute to achieving high performance. Our work demonstrates the effective combination of mathematical optimization and experimental research for the systematic development of high-performance solar devices. Our analysis also enhances the comprehension of the material science involved.

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