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자연과학대학
물리학전공
Journal papers
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Low-Cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields
Title
Low-Cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields
Authors
Tolborg
;
Kasper
;
Walsh
;
Aron
Ewha Authors
Aron Walsh
SCOPUS Author ID
Aron Walsh
Issue Date
2023
Journal Title
Journal of Physical Chemistry Letters
ISSN
1948-7185
Citation
Journal of Physical Chemistry Letters vol. 14, no. 51, pp. 11618 - 11624
Publisher
American Chemical Society
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
The rational design of alloys and solid solutions relies on accurate computational predictions of phase diagrams. The cluster expansion method has proven to be a valuable tool for studying disordered crystals. However, the effects of vibrational entropy are commonly neglected due to the computational cost. Here, we devise a method for including the vibrational free energy in cluster expansions with a low computational cost by fitting a machine learning force field (MLFF) to the relaxation trajectories available from cluster expansion construction. We demonstrate our method for two (pseudo)binary systems, Na1-xKxCl and Ag1-xPdx, for which accurate phonon dispersions and vibrational free energies are derived from the MLFF. For both systems, the inclusion of vibrational effects results in significantly better agreement with miscibility gaps in experimental phase diagrams. This methodology can allow routine inclusion of vibrational effects in calculated phase diagrams and thus more accurate predictions of properties and stability for mixtures of materials. © 2023 The Authors. Published by American Chemical Society
DOI
10.1021/acs.jpclett.3c03083
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