An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space.
- 1Department of Materials Science and Engineering, University of California, Berkeley, California94720, United States.
- 2Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States.
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.
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BibTeX
@article{gallant2026investigation,
title = {{An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space.}},
author = {Max C Gallant and David Mrdjenovich and Kristin A Persson},
journal = {Journal of chemical theory and computation},
year = {2026},
month = sep,
doi = {10.1021/acs.jctc.6c01114},
url = {https://europepmc.org/article/MED/42917452},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space.
AU - Max C Gallant
AU - David Mrdjenovich
AU - Kristin A Persson
PY - 2026
DA - 2026/09/01
JO - Journal of chemical theory and computation
DO - 10.1021/acs.jctc.6c01114
UR - https://europepmc.org/article/MED/42917452
AB - 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.
SN - 1549-9618
SN - 1549-9626
ER -