Machine learning analysis on stability of perovskite solar cells

In this work, a dataset containing long-term stability data for 404 organolead halide perovskite cells was constructed from 181 published papers and analyzed using machine-learning tools of association rule mining and decision trees; the effects of cell manufacturing materials, deposition methods and storage conditions on cell stability were investigated. For regular cells, mixed cation perovskites, multi-spin coating as one-step deposition, DMF + DMSO as precursor solution and chlorobenzene as anti-solvent were found to have positive effects on stability; SnO2 as ETL compact layer, PCBM as second ETL, inorganic HTLs or HTL-free cells, LiTFSI + TBP + FK209 and F4TCNQ as HTL additives and carbon as back contact were also found to improve stability. The cells stored under low humidity were found to be more stable as expected. The degradation was slightly faster in inverted cells under humid condition; the use of some materials (like mixed cation perovskites, PTAA and NiOx as HTL, PCBM + C60 as ETL, and BCP interlayer) were found to result in more stable cells.