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

• Published in: Chemical science (Cambridge) 2022-07, Vol.13 (28), p.8265-8273
• Main Authors: Shi, Yu, Lam, Stephen T, Beck, Thomas L
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Chemical potential; Chemistry; Density functional theory; Ion pairs; Ions; Liquids; Modelling; Molecular dynamics; Molten salts; Neural networks; Quantum chemistry; Simulation; Solvation; Thermodynamics
• ISSN: 2041-6520; 2041-6539
• DOI: 10.1039/d2sc02227c

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. Solvation thermodynamics in molten salt is accurately and efficiently predicted by combining ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and quasichemical theory (QCT).