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 the liquid phase, we demonstrate consistency whether we transmute one ion pair or the entire system into ideal gas particles. By locating the temperature where the chemical potential of solid and liquid phases cross, we predict a melting point of 880 ± 18 K for lithium chloride, which is remarkably close to the experimental value of 883 K. With this successful demonstration, we lay the foundation for highthroughput thermodynamic predictions of many properties that can be derived from the chemical potentials of the minority and majority components in molten salts.
| Original language | English |
|---|---|
| Pages (from-to) | 3078-3091 |
| Number of pages | 14 |
| Journal | Chemical Science |
| Volume | 16 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jan 24 2025 |
Funding
The authors would like to thank Thomas L. Beck (ORNL), Dilip Asthagiri (ORNL), and Juliano Schorne-Pinto (U. of South Carolina) for helpful discussions. The authors would also like to personally thank the NERSC staff, especially Neil Mehta, for his software support in assisting with parallel installation of DeePMD-kit with required modules on Perlmutter. This work was supported by the Office of Materials and Chemical Technologies within the Office of Nuclear Energy, U.S. Department of Energy. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC05-00OR22725. This research also used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under contract no. DE-AC02-05CH11231. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://www.energy.gov/doe-public-access-plan ).