A Study on the Mechanical Properties of Perovskite Films Based on Molecular Dynamics Simulation
Abstract
The bottleneck of service stability of perovskite solar cells is rooted in the mechanical failure of its active layer materials at the micro scale. In order to deeply understand this process, the nano-indentation mechanical response of all-inorganic perovskite CsPbBr3 under pre-stress was studied by molecular dynamics simulation at the atomic scale. The core of this research work is to systematically reveal the quantitative influence of prestress, an inevitable initial stress state in the preparation and service of practical devices, on the near-surface mechanical behavior of materials. Firstly, the stress–strain response of the CsPbBr3 model at 300 K, 350 K, and 400 K was verified. The temperature dependence of its mechanical properties and the consistency with the experimental values confirmed the reliability of the force field and simulation method. In addition, by applying a series of uniaxial pre-strains, we analyzed the influence of prestress on the evolution of the force-depth curve and indentation strain during nano-indentation. The results show that the introduction of pre-strain will induce the material to have a significant “softening effect” and systematically reduce its ability to resist the intrusion of the indenter. More importantly, this study quantitatively reveals the asymmetric influence of prestress direction: tensile prestress leads to more serious softening than compressive prestress with the same amplitude, indicating that materials are more prone to plastic deformation under tensile preload. This work clarifies the key regulation function of prestress on the mechanical properties of perovskite thin films and provides a crucial theoretical basis for constructing accurate cross-scale mechanical models and designing perovskite photoelectric devices with high reliability and fatigue resistance.
Fields of Study & Subjects
Perovskite Materials and Applications
Chemical and Physical Properties of Materials
Machine Learning in Materials Science
Publication details
- Journal / source
- Coatings
- Publisher
- MDPI AG
- Volume
- 16
- Issue
- 2
- Pages
- 212–212
- Publication date
- 6 February 2026
- Language
- EN