Revealing
the process–structure–property (PSP) relationships
of chemically complex mixed-ion perovskite requires comprehensive
insights into correlations between microstructures and chemical compositions.
However, experimentally determining the microstructural information
about complex perovskites over the composition space is a challenging
task. In this study, a machine learning enabled energy model was trained
for MA
y
FA1–y
Pb(Br
x
I1–x
)3 mixed-ion perovskite for fast and extensive sampling
over the compositional/permutational spaces to map the ion-mixing
energies, chemical ordering, and atomic strains. Correlation analysis
indicated the strong lattice distortion in the high-MA/Br concentration
regime is the primary reason for poor device performancestrong
lattice distortion induces high mixing energy, resulting in phase
segregation and defect formation. Hence, mitigating lattice distortion
to retain the single-phase solid solution is one necessary condition
of the optimal composition of mixed-ion perovskites. The present study
therefore provides insights into the microstructures as well as the
guidelines for determining the optimal composition of mixed-ion perovskite
materials.