An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space.
- DOI
- 10.1021/acs.jctc.6c01114
- Published
- 2026 Sep 22
- Container
- Journal of chemical theory and computation
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.6c01114,
title = {An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space.},
author = {Gallant MC and Mrdjenovich D and Persson KA},
year = {2026},
journal = {Journal of chemical theory and computation},
doi = {10.1021/acs.jctc.6c01114},
url = {https://doi.org/10.1021/acs.jctc.6c01114}
}RIS
TY - JOUR TI - An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space. AU - Gallant MC AU - Mrdjenovich D AU - Persson KA PY - 2026 JO - Journal of chemical theory and computation DO - 10.1021/acs.jctc.6c01114 UR - https://doi.org/10.1021/acs.jctc.6c01114 ER -
APA
MC, G., D, M., & KA, P. (2026). An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.6c01114
Source records
- pubmed · retrieved 2026-11-10T11:55:00.594Z