Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases.

Area of Science:

• Materials Science
• Computational Chemistry
• Crystallography

Background:

• Metastable materials exhibit valuable properties but are challenging to predict using traditional energy-based methods alone.
• Existing crystal structure prediction algorithms often overlook potentially synthesizable metastable phases that represent local thermodynamic equilibria.
• The limitations of purely energetic filtering hinder the discovery of diverse material structures.

Purpose of the Study:

• To develop and implement a novel computational approach for predicting metastable crystal structures.
• To integrate structural features, such as local order and symmetry, into evolutionary crystal structure searches.
• To identify new metastable materials with potential for innovative applications.

Main Methods:

• Developed a new method incorporating local crystalline order (coordination number, chemical environment) and symmetry (Bravais lattice, space group) for filtering candidate structures.
• Integrated this feature-based filtering with the XtalOpt evolutionary algorithm for crystal structure prediction.
• Benchmarked the method on known metastable systems: XeN8, brookite TiO2, and a high-pressure BaH4 phase.

Main Results:

• Successfully predicted known metastable phases, demonstrating the method's effectiveness.
• Identified a novel metastable melaminate salt, P1̅ WC3N6, with lower energy than previously proposed structures.
• The new phase P1̅ WC3N6 has an energy lower than two phases from a recent computational study.

Conclusions:

• The proposed method enhances the prediction of metastable materials by considering structural characteristics.
• This approach can aid in identifying the structures of already synthesized compounds.
• It offers a pathway for discovering new synthesis targets with desirable material properties.