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Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields

2025/07/10 by Xia Liang, Johan Klarbring, Liang, Xia +3 · 1 citation
Engineering · Materials Science · #FOS: Physical sciences #Heusler alloys: electronic and magnetic properties #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Perovskite Materials and Applications

paper · pdf · doi:10.48550/arxiv.2507.07926

openalex publication_date 2025/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Lead halide perovskites (APbX3) offer tunable optoelectronic properties but feature an intricate phase-stability landscape. Here we employ on-the-fly data collection and an equivariant message-passing neural-network potential to perform large-scale molecular dynamics of three prototypical perovskite systems: CsPbX3, MAPbX3, and FAPbX3. Integrating these simulations with the PDynA analysis toolkit, we resolve both equilibrium phase diagrams and dynamic structural evolution under varying temperature and halide-mixing conditions. Our findings reveal that the A-site cation strongly modulates octahedral tilt modes and phase pathways: MA+ effectively "forbids" the beta-to-gamma transition in MAPbX3 by requiring extensive molecular rearrangements and crystal rotation, whereas the debated low-temperature phase in FAPbX3 is best represented as an Im3 cubic phase with a+a+a+ tilts. Additionally, small changes in halide composition and arrangement \unicodex2013 from uniform mixing to partial segregation \unicodex2013 alter tilt correlations. Segregated domains can even foster anomalous tilting modes that impede uniform phase transformations. These results highlight the multi-scale interplay between cation environment and halide distribution, offering a rational basis for tuning perovskite architectures toward improved phase stability.

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