Low-Cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields.

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, Na_1-xK_xCl and Ag_1-xPd_x, for which accurate phonon dispersions and vibrational free energies are derived from the MLF

SUBMITTER: Tolborg K 

PROVIDER: S-EPMC10758113 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature