Ion Migration at Metal Halide Perovskite Grain Boundaries Elucidated with a Machine Learning Force Field DOI Creative Commons
Mikhail R. Samatov, Dongyu Liu, Long Zhao

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2024, Номер unknown, С. 12362 - 12369

Опубликована: Дек. 9, 2024

Metal halide perovskites are promising optoelectronic materials with excellent defect tolerance in carrier recombination, believed to arise largely from their unique soft lattices. However, weak lattice interactions also promote ion migration, leading serious stability issues. Grain boundaries (GBs) have been experimentally identified as the primary migration channels, but relevant mechanism remains elusive. Using molecular dynamics a machine learning force field, we directly model at common CsPbBr3 GB. We demonstrate that as-built GB model, containing 6400 atoms, experiences structural reconstruction over several nanoseconds, and only Br atoms diffuse after that. A fraction of near either migrate toward center or along through different channels. Increasing temperature not accelerates via Arrhenius activation allows more migrate. The energies much lower than bulk due large-scale distortions favorable non-stoichiometric local environments available GBs. Making composition stoichiometric by doping annealing can suppress migration. reported results provide valuable atomistic insights into properties metal perovskites.

Язык: Английский

Ion Migration at Metal Halide Perovskite Grain Boundaries Elucidated with a Machine Learning Force Field DOI Creative Commons
Mikhail R. Samatov, Dongyu Liu, Long Zhao

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2024, Номер unknown, С. 12362 - 12369

Опубликована: Дек. 9, 2024

Metal halide perovskites are promising optoelectronic materials with excellent defect tolerance in carrier recombination, believed to arise largely from their unique soft lattices. However, weak lattice interactions also promote ion migration, leading serious stability issues. Grain boundaries (GBs) have been experimentally identified as the primary migration channels, but relevant mechanism remains elusive. Using molecular dynamics a machine learning force field, we directly model at common CsPbBr3 GB. We demonstrate that as-built GB model, containing 6400 atoms, experiences structural reconstruction over several nanoseconds, and only Br atoms diffuse after that. A fraction of near either migrate toward center or along through different channels. Increasing temperature not accelerates via Arrhenius activation allows more migrate. The energies much lower than bulk due large-scale distortions favorable non-stoichiometric local environments available GBs. Making composition stoichiometric by doping annealing can suppress migration. reported results provide valuable atomistic insights into properties metal perovskites.

Язык: Английский

Процитировано

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