Computing chemical potentials with machine-learning-accelerated simulations to accurately predict thermodynamic properties of molten salts.

ABSTRACT: The successful design and deployment of next-generation nuclear technologies heavily rely on thermodynamic data for relevant molten salt systems. However, the lack of accurate force fields and efficient methods has limited the quality of thermodynamic predictions from atomistic simulations. Here we propose an efficient free energy framework for computing chemical potentials, which is the central free energy quantity behind many thermodynamic properties. We accelerate our simulations without sacrificing accuracy by using machine learning interatomic potentials trained on density functional theory (DFT) data. Using lithium chloride as our model system, we compute chemical potentials with DFT-accuracy for solid and liquid phases by transmuting ions into noninteracting particles. Notably, in t

SUBMITTER: Gibson LD 

PROVIDER: S-EPMC11758575 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature