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Abstract
We investigate the density isobar of water and the melting temperature of ice using six different density functionals. Machine-learning potentials are employed to ensure computational affordability. Our findings reveal significant discrepancies between various base functionals. Notably, even the choice of damping can result in substantial differences. Overall, the outcomes obtained through density functional theory are not entirely satisfactory across most utilized functionals. All functionals exhibit significant deviations either in the melting temperature or equilibrium volume, with most of them even predicting an incorrect volume difference between ice and water. Our heuristic analysis indicates that a hybrid functional with 25% exact exchange and van der Waals damping averaged between zero and Becke-Johnson dampings yields the closest agreement with experimental data. This study underscores the necessity for further enhancements in the treatment of van der Waals interactions and, more broadly, density functional theory to enable accurate quantitative predictions for molecular liquids.
Originalsprache | Englisch |
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Aufsatznummer | 131102 |
Seitenumfang | 8 |
Fachzeitschrift | Journal of Chemical Physics |
Jahrgang | 161 |
Ausgabenummer | 13 |
DOIs | |
Publikationsstatus | Veröffentlicht - 7 Okt. 2024 |
ÖFOS 2012
- 103015 Kondensierte Materie
- 102019 Machine Learning
Projekte
- 1 Laufend
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TACO: Taming Complexity in Materials Modeling
Diebold, U., Kresse, G., Mezger-Backus, E. H. G., Dellago, C. & Franchini, C.
1/03/21 → 28/02/25
Projekt: Forschungsförderung