Anion optimization for bifunctional surface passivation in perovskite solar cells

Abstract

Pseudo-halide (PH) anion engineering has emerged as a surface passivation strategy of interest for perovskite-based optoelectronics; but until now, PH anions have led to insufficient defect passivation and thus to undesired deep impurity states. The size of the chemical space of PH anions (>106 molecules) has so far limited attempts to explore the full family of candidate molecules. We created a machine learning workflow to speed up the discovery process using full-density functional theory calculations for training the model. The physics-informed machine learning model allowed us to pinpoint promising molecules with a head group that prevents lattice distortion and anti-site defect formation, and a tail group optimized for strong attachment to the surface. We identified 15 potential bifunctional PH anions with the ability to passivate both donors and acceptors, and through experimentation, discovered that sodium thioglycolate was the most effective passivant. This strategy resulted in a power-conversion efficiency of 24.56% with a high open-circuit voltage of 1.19 volts (24.04% National Renewable Energy Lab-certified quasi-steady-state) in inverted perovskite solar cells. Encapsulated devices maintained 96% of their initial power-conversion energy during 900 hours of one-sun operation at the maximum power point. Pseudo-halide anion engineering is an effective surface passivation strategy for perovskite-based optoelectronics but the large chemical space of molecules limits its potential. Here, the authors create a machine learning workflow to find optimized pseudo-halide anions, which are verified in devices with improved performances.