The goal of my research is to predict the degradation of encapsulated perovskite films and devices. Perovskite solar cells (PSCs) show potential for ultra-low manufacturing cost with high power conversion efficiency. However, the commercialization of PSCs is still uncertain given concerns about their stability. Previously, we showed how to predict the degradation of diffusion length in non-encapsulated MAPbI3 films over an extremely wide range of environment conditions using machine learning (ML). However, encapsulation is necessary for long service lifetimes. During the period of the award, I will collect a large dataset of degradation kinetics of encapsulated MAPbI3 and (FA,Cs)Pb(I,Br)3 perovskite films that includes in-situ optical transmittance along with video data of photoluminescence (PL) and scattering (via dark field microscopy). I will build a physiochemical kinetic model and use ML methods to yield predictions of the service life of perovskite films and devices.
Advisor: Hugh W. Hillhouse – Molecular Engineering