{
  "abstract": "Machine learning force fields (MLFFs) promise ab initio accuracy at a fraction of the computational cost of first-principles simulation, yet their fidelity for aqueous ion hydration across chemically diverse cation series remains systematically underexplored. Here, we benchmark an on-the-fly MLFF (OT-MLFF) framework, implemented in VASP via kernel-based Bayesian regression with adaptive DFT calls triggered when the estimated force error exceeds a set threshold, together with the pretrained, equivariant MACE foundation model applied off-the-shelf, against ab initio molecular dynamics (AIMD; CP2K, PBE-D3) and classical MD (CMD; Madrid-2019/TIP4P-2005) for aqueous solutions of Li+, Na+, K+, Cs+, Mg2+, and Ca2+. Across all six ions, the OT-MLFF reproduces a bulk water structure and the systematic increase of first-shell M-O distances down both cation series with near-AIMD accuracy, outperforming CMD. Coordination-number distributions reveal a mild OT-MLFF overcoordination of Li+ (modal CN 5 versus 4 in both AIMD and MACE) and Cs+, whereas the two divalent shells, including the previously problematic Ca2+, are reproduced by both machine-learned potentials, all three methods placing the Ca2+ coordination number at ≈6. MACE tracks AIMD most closely overall, its principal bias being a residual overcoordination of K+ and undercoordination of Cs+ in the diffuse monovalent shells. Angular distribution functions confirm accurate reproduction of the rigid octahedral geometry of Mg2+ but reveal systematic OT-MLFF smoothing of three-body features for more labile shells, including the O-Na-O shoulder near 65-70° and the O-Ca-O peak angle. The largest failures are kinetic: the OT-MLFF generates spurious first-shell water exchange for Mg2+, absent in AIMD, and completely suppresses the exchange of the dynamic Ca2+ shell, even though it reproduces the equilibrium coordination number of both ions, a clear dissociation between structural and dynamical fidelity. Velocity autocorrelation functions reveal a related failure, the OT-MLFF underestimating the cooperative cage confinement of the large monovalent ions Cs+ and K+. In each of these cases, the pretrained MACE model recovers the AIMD behavior that the on-the-fly potential misses. Collectively, these results establish that the OT-MLFF is robust for an equilibrium solvation structure but fails for three-body angular sensitivity and exchange kinetics in chemically distinct edge cases and that a deeper, higher-body-order, pretrained model such as MACE systematically narrows these gaps even without system-specific training. Targeted improvements such as augmented transition-state sampling, higher angular-resolution descriptors, and long-range electrostatic corrections are identified as high-priority directions for kinetic fidelity in next-generation electrolyte force fields.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, LondonE1 4NS, U.K."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mohammed S Salha"
    },
    {
      "affs": [
        0
      ],
      "name": "Wuyang Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "Gregory Chass",
      "orcid": "0000-0002-5265-6672"
    },
    {
      "affs": [
        0
      ],
      "name": "Devis Di Tommaso",
      "orcid": "0000-0002-4485-4468"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8syrxk3q3jfxd.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.jpcb.6c02837",
  "fulltext_url": "https://europepmc.org/articles/PMC13592074",
  "id": "8syrxk3q3jfxd",
  "issns": [
    "1520-6106",
    "1520-5207"
  ],
  "landing_url": "https://europepmc.org/article/MED/42915327",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7207871680",
  "pdf_url": "https://europepmc.org/articles/PMC13592074?pdf=render",
  "pmcid": "PMC13592074",
  "pmid": "42915327",
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8syrxk3q3jfxd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:02Z",
      "native_id": "MED:42915327",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42915327"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Benchmarking on-the-Fly Machine Learning Force Fields for Ion Hydration: Structure, Coordination, and Exchange Dynamics across Monovalent and Divalent Cations.",
  "title_text": "Benchmarking on-the-Fly Machine Learning Force Fields for Ion Hydration: Structure, Coordination, and Exchange Dynamics across Monovalent and Divalent Cations.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:02Z",
  "url": "https://research.draconitic.com/paper/8syrxk3q3jfxd",
  "venue": "The journal of physical chemistry. B",
  "versions": []
}