Machine Learning Helps Improve Perovskite Solar Cells
A team of researchers from HSE MIEM, LPI RAS, and the University of Southern California have applied machine learning to the analysis of internal defects in perovskite solar cells and proposed ways to improve their energy efficiency. The findings of the study performed on the Cs2AgBiBr6 double perovskite can be used to develop more efficient and durable perovskite-based materials. The paper has been published in the Journal of Physical Chemistry Letters.
7 September
2022