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

Abstract

Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to determine a structure's fitness, are not suitable for predicting the vast number of potentially synthesizable phases that represent a local minimum corresponding to a state in thermodynamic equilibrium. Here, we present a new approach for the prediction of metastable phases with specific structural features, and interface this method with the XtalOpt evolutionary algorithm. Our method relies on structural features that include the local crystalline order (e.g., the coordination number or chemical environment), and symmetry (e.g., Bravais lattice and space group) to filter the parent pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN${}_8$, with a two-dimensional polymeric nitrogen sublattice, brookite TiO${}_2$, and a high pressure BaH${}_4$ phase that was recently characterized. Additionally, a newly predicted metastable melaminate salt, $P$-1 WC${}_3$N${}_6$, was found to possess an energy that is lower than two phases proposed in a recent computational study. The method presented here could help in identifying the structures of compounds that have already been synthesized, and developing new synthesis targets with desired properties.

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Cited by in corpus (2)

• XtalOpt Version 13: Multi-Objective Evolutionary Search for Novel Functional Materials
• XtalOpt Version 14: Variable-Composition Crystal Structure Search for Functional Materials Through Pareto Optimization