DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning

22 May 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Molten alkali chloride salts are a critical component in concentrated solar power and nuclear applications. Despite their ubiquity, the extreme chemical reactivity of molten alkali chlorides at high temperatures has presented a significant challenge in characterizing atomic structures and dynamic properties experimentally. Here we investigate

molten NaCl by performing high temperature molecular dynamics simulations using a Gaussian Approximation Potential (GAP) trained on Density Functional Theory (DFT) datasets. Our GAP model, trained with a meager 1000 atomic configurations, arrives at near DFT accuracy with a mean absolute error of 1.5 meV/atom, thus enabling fast analysis of high temperature salt properties on large length (5000 ion pairs) and time (> 1ns) scales, currently inaccessible to ab initio simulations. Calculated structure factors and diffusion constants from our GAP model simulations show excellent agreement with experiments. Our results indicate that GAP models are able to capture the many-body interactions required to accurately model ionic-systems.


Molecular Dynamics Simulations
Machine Learning
Gaussian approximation potential
Pair distribution function
Molten salt
Concentrated Solar Power
density functional theory


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.