Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields
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arXiv
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| Natura: | Preprint |
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2025
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| _version_ | 1866908444881059840 |
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| author | Liang, Xia Klarbring, Johan Walsh, Aron |
| author_facet | Liang, Xia Klarbring, Johan Walsh, Aron |
| contents | Lead halide perovskites (APbX$_3$) 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: CsPbX$_3$, MAPbX$_3$, and FAPbX$_3$. 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 MAPbX$_3$ by requiring extensive molecular rearrangements and crystal rotation, whereas the debated low-temperature phase in FAPbX$_3$ is best represented as an Im$\bar{3}$ cubic phase with $a^+a^+a^+$ tilts. Additionally, small changes in halide composition and arrangement $\unicode{x2013}$ from uniform mixing to partial segregation $\unicode{x2013}$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07926 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields Liang, Xia Klarbring, Johan Walsh, Aron Materials Science Lead halide perovskites (APbX$_3$) 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: CsPbX$_3$, MAPbX$_3$, and FAPbX$_3$. 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 MAPbX$_3$ by requiring extensive molecular rearrangements and crystal rotation, whereas the debated low-temperature phase in FAPbX$_3$ is best represented as an Im$\bar{3}$ cubic phase with $a^+a^+a^+$ tilts. Additionally, small changes in halide composition and arrangement $\unicode{x2013}$ from uniform mixing to partial segregation $\unicode{x2013}$ 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. |
| title | Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields |
| topic | Materials Science |
| url | https://arxiv.org/abs/2507.07926 |