Correction to Density\nFunctional Theory Calculations\nand Machine Learning Interatomic Potentials for Molten Salts to Achieve\nExperimental Accuracy

Despite the considerable success\nof density functional\ntheory (DFT)\nin a broad class of materials, there are no exchange–correlation\nfunctionals or dispersion corrections that can systematically achieve\nhigh accuracy in molten salt simulations; for example, the density\nis often significantly underestimated. This study proposes a method\nto construct a correction potential that can fill the difference between\nDFT and experiments, using KCl as a test case. First, a machine learning\ninteratomic potential (MLIP) with DFT accuracy was constructed. Subsequently,\na correction potential was prepared to remove residual stresses brought\nby the MLIP at experimental densities. It was found that a small cation–anion\npairwise correction potential is sufficient to significantly improve\nnot only the density but also other material properties, suppressing\ncalculation errors to a level comparable to the deviation of experimental\ndata. This method is versatile and is expected to help realize experimental\naccuracy in molten salt simulations.