{"paper":{"title":"Ion-Specific Anomalous Water Diffusion in Aqueous Electrolytes: A Machine-Learned Many-Body Force Field Study with MACE","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A many-body machine-learned force field trained on DFT data reproduces the ion-specific anomaly in water diffusion for NaCl and CsI solutions.","cross_cats":["cond-mat.soft"],"primary_cat":"physics.chem-ph","authors_text":"Carlo Pierleoni, Ilnur Saitov, Isabella Daidone, Massimo Ciacchi, Nico Di Fonte","submitted_at":"2026-04-15T09:26:36Z","abstract_excerpt":"The dynamics of water in electrolyte solutions exhibits a striking, ion-specific anomaly: the diffusion coefficient of water is enhanced relative to the neat liquid in chaotropic CsI solutions, yet suppressed in kosmotropic NaCl solutions. This phenomenon, long challenging for classical force-field-based molecular dynamics, is studied here using classical molecular dynamics simulations with a many-body machine-learned force field (MLFF) trained within the MACE equivariant graph neural network framework. The force field is trained on energies, forces, and stresses computed at the density functi"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A many-body machine-learned force field trained on DFT data reproduces the ion-specific anomaly in water diffusion for NaCl and CsI solutions.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"998fcdae21cad955399fc7f1aff57e1323835911beaef037fc0e98dc0f31c706"},"source":{"id":"2604.13659","kind":"arxiv","version":3},"verdict":{"id":"ec928571-dc40-451e-b992-af5f8d701772","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T12:38:01.955068Z","strongest_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.","one_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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_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.","pith_extraction_headline":"A many-body machine-learned force field trained on DFT data reproduces the ion-specific anomaly in water diffusion for NaCl and CsI solutions."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.13659/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}