An Investigation in the Kinetic Persistence of TiO$_2$ Polymorphs using Machine Learning Driven Pathfinding in Crystal Configuration Space

TL;DR

This paper introduces a novel machine learning-based pathfinding method to analyze the kinetic stability of TiO₂ polymorphs by exploring their potential energy landscape, aiding the prediction of phase persistence.

Contribution

It develops a new graph-based algorithm using Crystal Normal Form to identify transformation pathways between metastable and stable TiO₂ phases.

Findings

The method successfully maps diffusionless transformation pathways in TiO₂.

It correlates pathway energetics with experimental phase stability.

The approach applies to both known and hypothetical TiO₂ polymorphs.

Abstract

As the number of theoretically predicted materials continues to grow, it becomes increasingly important to assess not only their thermodynamic stability but also their kinetic viability under realistic synthesis conditions. 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 lower energy phases. To accomplish this, we develop a new method for identifying diffusionless transformation pathways between metastable polymorphs and their ground-state counterparts and discuss the energetics of those pathways with respect to the experimental observation of each phase. This algorithm is underpinned by the recently developed Crystal Normal Form, which provides a graph representation of crystal configuration space and supplies the substrate for our pathfinding algorithm. We apply this method to the titanium dioxide system which contains the well-known anatase, rutile, and brookite phases in addition to a number of hypothetical metastable polymorphs.