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Metastable materials are abundant in nature and technology, showcasingremarkable properties that inspire innovative materials design. However,traditional crystal structure prediction methods, which rely solely onenergetic factors to determine a structure's fitness, are not suitable forpredicting the vast number of potentially synthesizable phases that represent alocal minimum corresponding to a state in thermodynamic equilibrium. Here, wepresent a new approach for the prediction of metastable phases with specificstructural features, and interface this method with the XtalOpt evolutionaryalgorithm. Our method relies on structural features that include the localcrystalline order (e.g., the coordination number or chemical environment), andsymmetry (e.g., Bravais lattice and space group) to filter the parent pool ofan evolutionary crystal structure search. The effectiveness of this approach isbenchmarked on three known metastable systems: XeN$_8$, with a two-dimensionalpolymeric nitrogen sublattice, brookite TiO$_2$, and a high pressure BaH$_4$phase that was recently characterized. Additionally, a newly predictedmetastable melaminate salt, $P$-1 WC$_{3}$N$_{6}$, was found to possess anenergy that is lower than two phases proposed in a recent computational study.The method presented here could help in identifying the structures of compoundsthat have already been synthesized, and developing new synthesis targets withdesired properties.

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