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

HA Chen and PH Tang and GJ Chen and CC Chang and CW Pao, JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 12, 3591-3599 (2021).

DOI: 10.1021/acs.jpclett.1c00410

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)FA(1-y)Pb(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.

Return to Publications page