Best practices for fitting machine learning interatomic potentials for molten salts: A case study using NaCl-MgCl2

• DOI: 10.1016/j.commatsci.2024.113409
• Publication Date: 2024-09-24T07:44:08Z
• Authors: Siamak Attarian; Chen Shen; Dane Morgan; Izabela Szlufarska
• Keywords: Chemical Physics (physics.chem-ph); Condensed Matter - Materials Science; Physics - Chemical Physics; Materials Science (cond-mat.mtrl-sci); FOS: Physical sciences

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 R2SCAN-D4 method calculates the thermophysical properties of NaCl and MgCl2 with an overall modestly better accuracy compared to the other three methods.<br/>40 Pages, 9 Figures<br/>