How machine learning can help select capping layers to suppress perovskite degradation [post]

Noor Titan Putri Hartono, Janak Thapa, Armi Tiihonen, Felipe Oviedo, Clio Batali, Jason Yoo, Zhe Liu, Ruipeng Li, David Fuertes Marrón, Moungi Bawendi, Tonio Buonassisi, Shijing Sun
2020 unpublished
Environmental stability of perovskite solar cells (PSCs) can be improved by a thin layer of low-dimensional (LD) perovskite sandwiched between the perovskite absorber and the hole transport layer (HTL). This layer, called 'capping layer,' has mostly been optimized by trial and error. In this study, we present a machine-learning framework to rationally design and optimize perovskite capping layers. We 'featurize' 21 organic halide salts, apply them as capping layers onto methylammonium lead
more » ... lammonium lead iodide (MAPbI<sub>3</sub>) thin films, age them under accelerated conditions combining illumination and increased humidity and temperature, and determine features governing stability using random forest regression and SHAP (SHapley Additive exPlanations). We find that a low number of hydrogen-bonding donors and a small topological polar surface area of the organic molecules correlate with increased MAPbI<sub>3</sub> film stability. The top performing organic halide salt, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI<sub>3</sub> stability lifetime by 4±2 times over bare MAPbI<sub>3</sub> and 1.3±0.3 times over state-of-the-art octylammonium bromide (OABr). Through morphological and synchrotron-based structural characterization, we found that this capping layer consists of a Ruddlesden-Popper perovskite structure and stabilizes the photoactive layer by "sealing off" the grain boundaries and changing the lead surface chemistry, through the suppression of lead (II) iodide (PbI<sub>2</sub>) formation and methylammonium loss.
doi:10.26434/chemrxiv.11766528 fatcat:pgutmfpyjndbfklpwwwgixau74