{
  "abstract": "In silico modeling of molten salts is pivotal for developing emerging carbon-free energy technologies. Accurate prediction of their thermophysical properties using first-principles methods remains computationally intractable owing to the large length and timescales required. Further, the choice of exchange-correlation (XC) functional critically governs the predictive accuracy of thermophysical properties. Here, we train machine learning force fields to investigate the thermophysical properties of NaBr, KCl, and AgCl, representing alkali- and transition-metal halides, across their solid and liquid phases, systematically benchmarking ten XC functionals for NaBr (600-1400 K) and testing the transferability of the best-performing functional to KCl and AgCl. These MLIPs enable prediction of densities and ionic diffusion coefficients at length and timescales beyond ab initio simulations, while capturing the first-order solid-liquid transition. We find that BEEF-vdW accurately predicts the optimized lattice constants of NaBr and KCl, consequently reproducing experimental solid and liquid densities, with Na+ and Br- self-diffusion coefficients agreeing with experiments to within ~5%. However, we find that the BEEF-vdW functional inaccurately predicts AgCl density owing to its inaccurate lattice constant, whereas PBE-D3 yields excellent density agreement (~5% deviation) across both phases. This work establishes a foundation for high-throughput generation of molten-salt thermophysical databases using general machine learning potentials.0",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, India, 400076"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Pritam Rudra"
    },
    {
      "affs": [
        0
      ],
      "name": "Sudarshan Vijay"
    },
    {
      "affs": [
        0
      ],
      "name": "Ojus Mohan"
    },
    {
      "name": "PRITAM RUDRA",
      "orcid": "0000-0002-8325-4123"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0rdr304vet3aq.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008809/v1",
  "fulltext_url": null,
  "id": "0rdr304vet3aq",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008809/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7213321936",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008809/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0rdr304vet3aq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008809",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008809/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Predicting Thermophysical Properties of Halide Salts Using On-the-Fly Machine-Learned Interatomic Potentials Across Multiple Exchange-Correlation Functionals",
  "title_text": "Predicting Thermophysical Properties of Halide Salts Using On-the-Fly Machine-Learned Interatomic Potentials Across Multiple Exchange-Correlation Functionals",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/0rdr304vet3aq",
  "venue": "ChemRxiv",
  "versions": []
}