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.
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Quantum chemical accuracy from density functional approximations via machine learning
Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol−1with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol−1) on test data. Moreover, density-based
- Award ID(s):
- 1856165
- PAR ID:
- 10305325
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Reaction of {LiC6H2−2,4,6‐Cyp3⋅Et2O}2(Cyp=cyclopentyl) (
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