Issue 1, 2023

Grain boundary sliding and distortion on a nanosecond timescale induce trap states in CsPbBr3: ab initio investigation with machine learning force field

Abstract

Grain boundaries (GBs) in perovskite solar cells and optoelectronic devices are widely regarded as detrimental defects that accelerate charge and energy losses through nonradiative carrier trapping and recombination, but the mechanism is still under debate owing to the diversity of GB configurations and behaviors. We combine ab initio electronic structure and machine learning force field to investigate evolution of the geometric and electronic structure of a CsPbBr3 GB on a nanosecond timescale, which is comparable with the carrier recombination time. We demonstrate that the GB slides spontaneously within a few picoseconds increasing the band gap. Subsequent structural oscillations dynamically produce midgap trap states through Pb–Pb interactions across the GB. After several hundred picoseconds, structural distortions start to occur, increasing the occurrence of deep midgap states. We identify a distinct correlation of the average Pb–Pb distance and fluctuations in the ion coordination numbers with the appearance of the midgap states. Suppressing GB distortions through annealing and breaking up Pb–Pb dimers by passivation can efficiently alleviate the detrimental effects of GBs in perovskites. The study provides new insights into passivation of the detrimental GB defects, and demonstrates that structural and charge carrier dynamics in perovskites are intimately coupled.

Graphical abstract: Grain boundary sliding and distortion on a nanosecond timescale induce trap states in CsPbBr3: ab initio investigation with machine learning force field

Supplementary files

Article information

Article type
Paper
Submitted
24 Oct 2022
Accepted
05 Dec 2022
First published
05 Dec 2022

Nanoscale, 2023,15, 285-293

Author version available

Grain boundary sliding and distortion on a nanosecond timescale induce trap states in CsPbBr3: ab initio investigation with machine learning force field

D. Liu, Y. Wu, A. S. Vasenko and O. V. Prezhdo, Nanoscale, 2023, 15, 285 DOI: 10.1039/D2NR05918E

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