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Size dependent solid-solid crystallization of halide perovskites

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arxiv 2404.05644 v1 pith:YPA2NJAU submitted 2024-04-08 cond-mat.mtrl-sci

classification cond-mat.mtrl-sci
keywords crystallizationperovskitesimulationssolid-solidphasecellsdynamicsfaceted
verification ladder T0 review T1 audit T2 compute T3 formal
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The efficiency and stability of halide perovskite-based solar cells and light-emitting diodes directly depend on the intricate dynamics of solid-solid crystallization[1-23]. In this study, we employ a multi-scale approach using random phase approximation, density functional theory, machine learning potentials, reduced charge force fields, and both enhanced sampling biased and brute-force unbiased molecular dynamics simulations to understand the solid-solid phase transitions in cesium lead iodide perovskite. Our simulations uncover that the direct phase transition from the non-perovskite to the perovskite involves the formation of stacked-faulted and low-dimensional intermediate structures. Through extensive large-scale all-atom simulations encompassing up to 650,000 atoms, we observe that solid-solid crystallization may require the formation of a sufficiently large critical nucleus to grow into a faceted perovskite crystal. Based on simulations, we determine that utilizing (100)-faceted seeded crystallization could offer a promising path for manufacturing high-performance and stable perovskite solar cells.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Transformer Atomic Cluster Expansion: TRACE

    cond-mat.mtrl-sci 2026-07 conditional novelty 6.0 of 10

    A no-message-passing, attention-based local potential reproduces a perovskite phase transition, liquid-water O–O structure, and an organic rearrangement barrier with a single architecture.

  2. Advances in modeling complex materials: The rise of neuroevolution potentials

    cond-mat.mtrl-sci 2025-01 conditional novelty 4.0 of 10

    Neuroevolution potentials, trained with an evolutionary strategy and running on GPUs, match or approach quantum-accurate energies and forces while simulating systems with millions of atoms, at speeds far beyond compet...

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