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

Hsin-An Chen¹, Ping-Han Tang¹, Guan-Jie Chen²

¹Research Center for Applied Sciences, Academia Sinica, Taipei, Taiwan.

The Journal of Physical Chemistry Letters

|April 6, 2021

Summary

Understanding mixed-ion perovskite materials requires linking their structure and properties. This study uses machine learning to map ion mixing and reveals lattice distortion as key to performance, guiding optimal composition discovery.

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Area of Science:

Materials Science
Solid-State Chemistry
Computational Materials Science

Background:

Establishing process-structure-property (PSP) relationships in complex mixed-ion perovskites is crucial for material development.
Experimental determination of microstructural information across the composition space of these materials presents significant challenges.

Purpose of the Study:

To develop a machine learning-enabled energy model for comprehensive sampling of compositional and permutational spaces in mixed-ion perovskites.
To investigate the correlations between microstructures, chemical compositions, and resulting properties of MAFA_1-Pb(BrI_1-)₃ perovskites.
To provide guidelines for determining optimal compositions of mixed-ion perovskite materials.

Main Methods:

Training a machine learning-enabled energy model for fast and extensive sampling of MAFA_1-Pb(BrI_1-)₃ perovskite systems.
Mapping ion-mixing energies, chemical ordering, and atomic strains across compositional and permutational spaces.
Performing correlation analysis to link microstructural features with device performance.

Main Results:

Identified strong lattice distortion in high methylammonium (MA)/bromine (Br) concentration regimes as a primary cause of poor device performance.
Demonstrated that significant lattice distortion leads to high mixing energy, promoting phase segregation and defect formation.
Established a direct correlation between lattice distortion, mixing energy, and the stability of the single-phase solid solution.

Conclusions:

Mitigating lattice distortion is essential for retaining a single-phase solid solution, a necessary condition for optimal mixed-ion perovskite composition.
The study provides critical insights into the microstructural factors governing the performance of mixed-ion perovskites.
Developed guidelines for the rational design and selection of optimal mixed-ion perovskite compositions.