Modeling Metal‐Halide Perovskite Thin Film: Deterministic Voronoi Diagrams Based on Stochastic Correlated Nuclei

Abstract Metal‐halide perovskite thin films, composed of grains, exhibit microstructures with regularity and intriguing geometries. However, the underlying geometric principles shaping these patterns remain unclear. In this study, the geometric and statistical properties of grain structures are investigated. Grain shapes are characterized by using equivalent radius and shape parameters such as circularity, aspect ratio, convexity, and solidity. These shape parameters with those of Voronoi diagrams generated from various models are compared, including correlated and uncorrelated center distributions. These findings reveal that the statistical properties, including means and standard deviation of the shape parameters, align closely with Voronoi diagrams based on random close packing of circle centers. This suggests that such Voronoi diagrams provide an accurate mathematical model for describing perovskite thin‐film morphology. Furthermore, by integrating the correlated center distribution and Voronoi framework with the Avrami–Johnson–Mehl method, a model is proposed for nucleation, grain growth, and morphology evolution in perovskite thin films. This work offers a foundational perspective for understanding the film formation mechanisms of perovskite thin films, paving the way for enhanced control over their microstructures.

Authors

Institutions

Publication Details

Journal
Advanced Theory and Simulations
Published
2025-03-08
DOI
https://doi.org/10.1002/adts.202500115
Citations
1
Primary Topic
Perovskite Materials and Applications
Type
article
Field-Weighted Citation Impact
0.73

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling Metal‐Halide Perovskite Thin Film: Deterministic Voronoi Diagrams Based on Stochastic Correlated Nuclei

Qing Gong Song, Q. Cheng, Deli Li, Zhèn Yáng et al.
1 citations
Advanced Theory and Simulations
Perovskite Materials and Applications
0.73
article

Modeling Metal‐Halide Perovskite Thin Film: Deterministic Voronoi Diagrams Based on Stochastic Correlated Nuclei

Qing Gong Song, Q. Cheng, Deli Li, Zhèn Yáng, Yue Wang, Yonghua Chen, Yang Liu, Janet Pan, Zhiyun Zeng
article en
1 citations

Abstract

Abstract Metal‐halide perovskite thin films, composed of grains, exhibit microstructures with regularity and intriguing geometries. However, the underlying geometric principles shaping these patterns remain unclear. In this study, the geometric and statistical properties of grain structures are investigated. Grain shapes are characterized by using equivalent radius and shape parameters such as circularity, aspect ratio, convexity, and solidity. These shape parameters with those of Voronoi diagrams generated from various models are compared, including correlated and uncorrelated center distributions. These findings reveal that the statistical properties, including means and standard deviation of the shape parameters, align closely with Voronoi diagrams based on random close packing of circle centers. This suggests that such Voronoi diagrams provide an accurate mathematical model for describing perovskite thin‐film morphology. Furthermore, by integrating the correlated center distribution and Voronoi framework with the Avrami–Johnson–Mehl method, a model is proposed for nucleation, grain growth, and morphology evolution in perovskite thin films. This work offers a foundational perspective for understanding the film formation mechanisms of perovskite thin films, paving the way for enhanced control over their microstructures.

Advanced Theory and SimulationsVol. 8(6)
Fujian Normal University (CN), Nanjing Tech University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 31%
Perovskite Materials and Applications
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.