Pith Number

pith:HCBNR4AY

pith:2026:HCBNR4AYNXNEHPAQRA5NVCREVK

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Ion-Specific Anomalous Water Diffusion in Aqueous Electrolytes: A Machine-Learned Many-Body Force Field Study with MACE

Carlo Pierleoni, Ilnur Saitov, Isabella Daidone, Massimo Ciacchi, Nico Di Fonte

A many-body machine-learned force field trained on DFT data reproduces the ion-specific anomaly in water diffusion for NaCl and CsI solutions.

arxiv:2604.13659 v3 · 2026-04-15 · physics.chem-ph · cond-mat.soft

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Claims

C1strongest claim

Simulations of NaCl and CsI aqueous solutions at ambient conditions over a concentration range of 0.89--3.56 mol/kg reproduce the experimentally observed anomalous diffusion and show a quantitative improvement over previous results obtained with the DeePMD framework, particularly for NaCl solutions.

C2weakest assumption

The revPBE-D3 exchange-correlation functional provides a reliable balance between accuracy and computational efficiency for aqueous systems, allowing the trained MACE model to correctly capture many-body interactions and dynamics in the electrolyte solutions.

C3one line summary

MACE-based simulations reproduce the experimental anomaly of enhanced water diffusion in CsI solutions and suppressed diffusion in NaCl solutions, attributing the effects to differences in first and second hydration shells.

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1 paper in Pith

Ion-Specific Anomalous Water Diffusion in Aqueous Electrolytes: A Machine-Learned Many-Body Force Field Study with MACE

Receipt and verification

First computed	2026-06-24T01:15:02.767229Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

3882d8f0186dda43bc10883ada8a24aa9f6727fe6971bb45af3c9fb6add87de4

Aliases

arxiv: 2604.13659 · arxiv_version: 2604.13659v3 · doi: 10.48550/arxiv.2604.13659 · pith_short_12: HCBNR4AYNXNE · pith_short_16: HCBNR4AYNXNEHPAQ · pith_short_8: HCBNR4AY

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/HCBNR4AYNXNEHPAQRA5NVCREVK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 3882d8f0186dda43bc10883ada8a24aa9f6727fe6971bb45af3c9fb6add87de4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "025e4fd4c6d1699ed001416d11cc7c3cafa9671eefb79eafe95103cd5d2c9b52",
    "cross_cats_sorted": [
      "cond-mat.soft"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.chem-ph",
    "submitted_at": "2026-04-15T09:26:36Z",
    "title_canon_sha256": "acbb12e703ff9cd4fc3eae115d435e4c6203bf2ccef8e4c7490e9739a6577285"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.13659",
    "kind": "arxiv",
    "version": 3
  }
}