Communication: Modeling layered mosaic perovskite alloy microstructures across length scales via a packing algorithm

Area of Science:

• Materials Science
• Computational Materials Science
• Solid-State Chemistry

Background:

• Layered metal-halide perovskites exhibit diverse microstructures tunable by B-site composition.
• Traditional ab initio methods are computationally intensive and limited to small sample sizes for modeling these materials.

Purpose of the Study:

• To develop a computationally efficient algorithm for modeling large-scale layered perovskite alloys.
• To accurately determine geometrical and topological properties of B-site arrangements in perovskite inorganic layers.
• To enable exploration of hypothetical layered mosaic alloy compositions for desired properties.

Main Methods:

• A hard-particle packing algorithm was developed to model large samples of layered complex alloys.
• The algorithm determines geometrical and topological properties of B-site arrangements across length scales.
• A "mixing" metric was employed to quantify the degree of mixing in simulated structures.

Main Results:

• The algorithm accurately predicts B-site arrangements and miscibility in layered alloys, consistent with experimental data.
• The model captures complex dynamics like thermal motion, octahedral tilting, and bond variations.
• Simulations provided insights into experimentally measured magnetic properties of copper-indium systems.

Conclusions:

• The developed hard-particle packing algorithm offers an efficient alternative to ab initio methods for modeling layered perovskites.
• The algorithm and mixing metric facilitate the exploration of vast compositional spaces for novel optoelectronic and magnetic materials.
• The approach is generalizable to 3D perovskite alloys.