How machine learning can help select capping layers to suppress perovskite degradation.

ABSTRACT: Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI₃) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules' low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI₃ film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEA

SUBMITTER: Hartono NTP 

PROVIDER: S-EPMC7441172 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature