Best Practices for Fitting Machine Learning Interatomic Potentials for Molten Salts - A Case Study Using NaCl-MgCl2

Aug 6, 2026

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Developed a compositionally transferable machine learning interatomic potential for the (NaCl)1-x(MgCl2)x molten-salt pseudo-binary system using atomic cluster expansion with PBE-D3, showing a robust fit achievable by training on only x = {0, 1/3, 2/3, 1}. Evaluated multiple DFT levels—PBE-D3, PBE-D4, R2SCAN-D4, and R2SCAN-rVV10—on unary NaCl and MgCl2. Results indicate R2SCAN-D4 yields overall modestly improved thermophysical-property accuracy versus the other methods.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages50

Reading Time2 h 6 min

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Introduction

Challenges of experimental characterization

Computational approaches for molten-salt properties

ML interatomic potentials and training-data design

Best Practices for Fitting Machine Learning Interatomic Potentials for Molten Salts: A Case Study Using NaCl-MgCl2 SiamakAttarian a*, Chen Shen a*, Dane Morgan a*, Izabela Szlufarska a* a Department of Materials Science and Engineering, University of Wisconsin, 1509 University Ave, Madison, WI, 53706, USA * * [sattarian@wisc.edu](sattarian@wisc.edu)[cshen89@wisc.edu](cshen89@wisc.edu) * [ddmorgan@wisc.edu](ddmorgan@wisc.edu) * [szlufarska@wisc.edu](szlufarska@wisc.edu) Keywords: Molten Salts, Compositionally Transferable Interatomic Potentials, Atomic Cluster Expansion, Dispersion Corrections Abstract In this work, we developed a compositionally transferable machine learning interatomic potential using atomic cluster expansion potential and PBE-D3 method for (NaCl)1-x(MgCl2)x molten salt and we showed that it is possible to fit a robust potential for this pseudo-binary system by only including data from x={0, 1/3, 2/3, 1} . We also assessed the performance of several DFT methods including PBE-D3, PBE-D4, R2SCAN-D4, and R2SCAN-rVV10 on unary NaCl and MgCl2 salts. Our results show that the R2SCAND4 method calculates the thermophysical properties of NaCl and MgCl2 with an overall modestly better accuracy compared to the other three methods. 1. Introduction Molten salts are materials with prospective applications in renewable energy systems such as heat transfer medium in molten salt reactors and heat storage materials in concentrated solar powerplants [1–3] . Researchers are actively investigating the thermophysical and electrochemical properties of molten salts to find or design multicomponent salts that have a certain set of properties (ex. low melting point, low corrosivity, high specific heat capacity, low viscosity, etc.) tailored to certain applications [4–8] . As the main application of these salts is at high temperatures and impurities are difficult to control, the experimental characterization of the desired properties is challenging. Therefore, few experimental data are available and those mostly for unary salts (we will refer to salts by their number of cation components, e.g., NaCl is unary, NaCl-MgCl2 is binary, etc.) . Considering the vast design space of multicomponent salts, achieving a large database of the thermophysical properties of salts seems impractical through experimental means. Computational methods are inexpensive and faster alternatives to explore the properties of multicomponent salts. Among the available computational methods, ab initio molecular dynamics (AIMD) simulation using density functional theory (DFT) is the most accurate method commonly used to investigate some properties of molten salts [9,10] . Since AIMD calculation is computationally expensive, the sizes ofthe systems are usually limited to a few hundred atoms and the simulation times to a few tens of picoseconds, which allows one to calculate many properties, such as density, specific heat, etc., with acceptable uncertainties. However, for some properties, such as viscosity, the AIMD cannot reach adequate time scales to reduce uncertainties to an acceptable level for most applications. MD simulations with classical interatomic potentials [11,12] are low-cost options but the accuracies are questionable and fitting the parameters of the classical potentials is not an easy task. Recent advances in machine learning interatomic potentials (MLIPs) have made it possible to run MD simulations of large systems for long time scales with ab-initio level accuracies [13,14] . MLIPs are also easy to fit as they mostly rely on robust optimization methods that have been welldeveloped in the computer science community. An important step in fitting MLIPs is the collection of fitting data. MLIPs are fitted to data calculated by ab-initio methods (usually DFT) and generating enough relevant DFT data that would fit a robust potential has been a concern since MLIPs were introduced. Specifically in the earlier attempts to fit ML

FAQs

What machine learning interatomic potential approach is used for the NaCl-MgCl2 molten-salt system?

The study builds a compositionally transferable ML interatomic potential using atomic cluster expansion with PBE-D3 as the training-data basis for (NaCl)1-x(MgCl2)x.

How many compositions were included to fit a transferable potential across the pseudo-binary range?

Only four compositions were included: x = {0, 1/3, 2/3, 1}, and the paper shows this is sufficient for a robust potential for the full range.

Which DFT method provided the most accurate thermophysical properties for unary NaCl and MgCl2?

R2SCAN-D4 produced overall modestly better accuracy than PBE-D3, PBE-D4, and R2SCAN-rVV10.

Aug 6, 2026

Upload by Maeve