Sampling Polymorphs of Ionic Solids using Random Superlattices.

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Physics

Background:

• Polymorphism in materials science presents a vast, underexplored avenue for novel functional material discovery.
• Existing methods for exploring polymorphs and their stability are limited, necessitating new approaches.

Purpose of the Study:

• To develop and present a novel computational approach for exploring the space of material polymorphs.
• To assess the realizability of predicted polymorphs, particularly for partially ionic solids.

Main Methods:

• Utilized local density functional theory (DFT) relaxations on a large set of random supperlattices (RSLs).
• Employed a distribution strategy within RSLs to favor cation-anion coordination.
• Applied the RSL sampling method to Magnesium Oxide (MgO), Zinc Oxide (ZnO), and Tin Dioxide (SnO2).

Main Results:

• The probability of a structure's occurrence within the RSL sampling serves as a measure of its realizability.
• Successfully explained the existence of experimentally observed, metastable polymorphs in MgO, ZnO, and SnO2.
• Demonstrated the efficacy of the RSL approach in predicting stable and metastable material structures.

Conclusions:

• The presented RSL method provides a robust framework for exploring material polymorphism and predicting structure realizability.
• This approach facilitates the discovery of novel functional materials by systematically assessing potential polymorphs.
• The findings offer a pathway to understanding and predicting metastable phases in functional materials.