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.
%0 Journal Article
%1 Jung2023
%A Jung, Jong Hyun
%A Srinivasan, Prashanth
%A Forslund, Axel
%A Grabowski, Blazej
%D 2023
%J npj Computational Materials
%K EXC2075 PN3 PN3A-4 slected
%N 1
%P 3
%R 10.1038/s41524-022-00956-8
%T High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials
%U https://doi.org/10.1038/s41524-022-00956-8
%V 9
%X 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.
@article{Jung2023,
abstract = {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.},
added-at = {2025-02-14T11:17:09.000+0100},
author = {Jung, Jong Hyun and Srinivasan, Prashanth and Forslund, Axel and Grabowski, Blazej},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/222efac933067c17e4cd9b0e1ecd366ec/simtechpuma},
day = 10,
doi = {10.1038/s41524-022-00956-8},
interhash = {cb8f6c71f4734d3fe915dd0dccf2086f},
intrahash = {22efac933067c17e4cd9b0e1ecd366ec},
issn = {2057-3960},
journal = {npj Computational Materials},
keywords = {EXC2075 PN3 PN3A-4 slected},
month = {01},
number = 1,
pages = 3,
timestamp = {2025-02-14T11:17:09.000+0100},
title = {High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials},
url = {https://doi.org/10.1038/s41524-022-00956-8},
volume = 9,
year = 2023
}
