Microstructure Maps of Complex Perovskite Materials from Extensive Monte Carlo Sampling Using Machine Learning Enabled Energy Model
Journal
Journal of Physical Chemistry Letters
Journal Volume
12
Journal Issue
14
Pages
3591-3599
Date Issued
2021
Author(s)
Abstract
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. ? 2021 American Chemical Society.
Subjects
Ions; Machine learning; Microstructure; Mixing; Monte Carlo methods; Turing machines; Chemical compositions; Complex perovskite materials; Complex perovskites; Correlation analysis; Lattice distortions; Microstructural information; Monte Carlo sampling; Optimal composition; Perovskite; article; correlation analysis; machine learning; practice guideline
SDGs
Type
journal article