An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space

Abstract

As the number of theoretically predicted materials continues to expand, it is increasingly important to evaluate not only their thermodynamic stability but also their kinetic resilience against transformation to competing polymorphs. In this study, we investigate the hypothesis that the kinetic persistence of a metastable polymorph is related to the topography of the potential energy landscape separating it from the lower-energy phases. To accomplish this, we developed a new method for identifying diffusionless transformation pathways between metastable polymorphs and their ground-state counterparts and discussed the energetics of those pathways with respect to the experimental observations of each phase. This algorithm utilizes the recently developed crystal normal form, which provides a graph representation of the crystal configuration space and supplies the substrate for our pathfinding algorithm. We apply this method to the TiO2 system, which contains the well-known anatase, rutile, and brookite phases, in addition to a number of hypothetical metastable polymorphs and high-pressure phases.