Deep neural network based quantum simulations and quasichemical theory for accurate modeling of molten salt thermodynamics

Yu Shi, Stephen T. Lam, Thomas L Beck

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

With dual goals of efficient and accurate modeling of solvation thermodynamics in molten salt liquids, we employ ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and quasichemical theory (QCT) to calculate the excess chemical potentials for the solute ions Na+ and Cl in the molten NaCl liquid. NNIP-based molecular dynamics simulations accelerate the calculations by 3 orders of magnitude and reduce the uncertainty to 1 kcal mol−1. Using the Density Functional Theory (DFT) level of theory, the predicted excess chemical potential for the solute ion pair is −178.5 ± 1.1 kcal mol−1. A quantum correction of 13.7 ± 1.9 kcal mol−1 is estimated via higher-level quantum chemistry calculations, leading to a final predicted ion pair excess chemical potential of −164.8 ± 2.2 kcal mol−1. The result is in good agreement with a value of −163.5 kcal mol−1 obtained from thermo-chemical tables. This study validates the application of QCT and NNIP simulations to the molten salt liquids, allowing for significant insights into the solvation thermodynamics crucial for numerous molten salt applications.

Original languageEnglish
Pages (from-to)8265-8273
Number of pages9
JournalChemical Science
Volume13
Issue number28
DOIs
StatePublished - Jun 15 2022

Funding

We acknowledge NSF grants CHE-1565632 and CHE-1955161 at the University of Cincinnati for financial support of this research. The computations were performed at the Ohio Supercomputer Center and the Advanced Research Computing Center in University of Cincinnati. Y. Shi acknowledges the support of the College of Arts and Sciences at the University of Cincinnati. S. Lam is supported by the Department of Energy, Office of Nuclear Energy, Nuclear Energy University Program under award no. DE-NE0009204. Research at Oak Ridge National Laboratory is supported under contract DE-AC05-00OR22725 from the U.S. Department of Energy to UT-Battelle, LLC. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE ASCR Office of Science User Facility supported under Contract DE-AC05-000R22725.

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