Machine Learning-Enabled Optical Architecture Design of Perovskite Solar Cells.

J Phys Chem Lett

Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing 100081, China.

Published: April 2024

Perovskite solar cells, emerging as a cutting-edge solar energy technology, have demonstrated a power conversion efficiency (PCE) of >26%, which is below the theoretical limit of 33%. This study, employing a combination of neural network models and high-throughput simulation calculations, taking the single-junction FAPbI cell as an illustrative example, indicates that a pyramid structure, in comparison of a planar one, can increase the highest to 27.4 mA/cm and the PCE to 28.4%. Both and PCE surpass the currently reported experimental results. The optimized periodicity and tilt angle of the pyramid structure align with the textured structure of crystalline silicon solar cells. These results underscore the substantial development potential of neural network inverse design based on high-throughput calculations in the field of optoelectronic devices and provide theoretical guidance for the design of monolithic perovskite-silicon tandem solar cells.

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http://dx.doi.org/10.1021/acs.jpclett.4c00320DOI Listing

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