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http://dx.doi.org/10.18419/opus-13480
Autor(en): | Jung, Jong Hyun Srinivasan, Prashanth Forslund, Axel Grabowski, Blazej |
Titel: | High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials |
Erscheinungsdatum: | 2023 |
Dokumentart: | Zeitschriftenartikel |
Seiten: | 12 |
Erschienen in: | npj computational materials 9 (2023), No. 3 |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-134991 http://elib.uni-stuttgart.de/handle/11682/13499 http://dx.doi.org/10.18419/opus-13480 |
ISSN: | 2057-3960 |
Zusammenfassung: | Accurate prediction of thermodynamic properties requires an extremely accurate representation of the free-energy surface. Requirements are twofold - first, the inclusion of the relevant finite-temperature mechanisms, and second, a dense volume–temperature grid on which the calculations are performed. A systematic workflow for such calculations requires computational efficiency and reliability, and has not been available within an ab initio framework so far. Here, we elucidate such a framework involving direct upsampling, thermodynamic integration and machine-learning potentials, allowing us to incorporate, in particular, the full effect of anharmonic vibrations. The improved methodology has a five-times speed-up compared to state-of-the-art methods. We calculate equilibrium thermodynamic properties up to the melting point for bcc Nb, magnetic fcc Ni, fcc Al, and hcp Mg, and find remarkable agreement with experimental data. A strong impact of anharmonicity is observed specifically for Nb. The introduced procedure paves the way for the development of ab initio thermodynamic databases. |
Enthalten in den Sammlungen: | 03 Fakultät Chemie |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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s41524-022-00956-8.pdf | 1,95 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons