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

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 (, the coordination number or chemical environment), and symmetry (, Bravais lattice and space group) to filter the breeding pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN, with a two-dimensional polymeric nitrogen sublattice, brookite TiO, and a high pressure BaH phase, which was recently characterized. Additionally, a newly predicted metastable melaminate salt, 1̅ WCN, was found to possess an energy that is lower than that of 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 in developing new synthesis targets with desired properties.