Microstructure Maps of Complex Perovskite Materials from Extensive Monte Carlo Sampling Using Machine Learning Enabled Energy Model.

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 MAyFA1-yPb(BrxI1-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 performance-strong 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.