Publication date: 11th March 2026
Halide perovskites have emerged as promising materials for next-generation optoelectronic devices, particularly solar cells, owing to their excellent optoelectronic properties and compositional tunability. A key characteristic of these materials is their soft and polarizable lattice, which enables structural flexibility and defect tolerance. However, this same lattice softness also leads to intrinsic instability, as it facilitates defect formation and lowers the barriers for ion migration. Understanding ionic transport across different compositions and length scales is therefore essential for improving device performance and long-term stability.
At the atomistic level, ion migration is investigated using density functional theory (DFT) together with the climbing image nudged elastic band (CI-NEB) method. Migration barriers for halide ions in CsPbX3 (X = I, Br, Cl) are systematically evaluated, and their sensitivity to computational parameters is analysed. The results show a strong dependence of migration barriers on the choice of exchange–correlation functional, defect charge state, and inclusion of spin–orbit coupling. This establishes a consistent framework for comparing migration barriers across compositions and identifying intrinsic material trends.
To extend the analysis to finite temperatures and larger length scales, machine-learned interatomic potentials based on the neuroevolution potential (NEP) framework are developed and employed in molecular dynamics simulations of mixed systems Cs{1-y}RbyPb(Br{1-x}Ix)3. Diffusion coefficients are extracted from mean squared displacements over a wide temperature range and analysed using the Arrhenius relation, allowing the separation of energetic and dynamical contributions to ionic transport. The results show that iodine diffuses faster than bromine across mixed compositions, consistent with weaker Pb–I bonding and lower migration barriers.
Overall, this combined first-principles and machine-learning approach provides a consistent and scalable framework for understanding ionic transport in halide perovskites, bridging atomistic migration mechanisms and macroscopic diffusion behaviour. These insights highlight the role of compositional engineering in controlling ion migration and improving the stability of perovskite-based devices.
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