{
  "abstract": "This fully synthetic study tests a structure-preserving surrogate on NRTL-generated phase equilibrium systems; it contains no experimental molecular validation. A symmetric neural potential represents GE/(nRT), from which activity coefficients, thermal derivatives, binodals, flashes, and tangent-plane-distance diagnostics are derived. Across ten binary training seeds, its median lnγ MAE was 1.16×10−3, better than matched direct regression (2.40×10−3) but not Redlich–Kister (9.65×10−4) or correctly specified refitted NRTL (2.36×10−4). Gibbs–Duhem residuals decayed as O(h2) under grid refinement, confirming diagnostic truncation around an analytic identity. A 41,600-point binary hull gave endpoint MAE 2.31×10−4. On 100 ternary type-I feeds, phase-count agreement with numerical reference labels was 98.0% (exact 95% interval, 93.0–99.8%), with two false negatives after recovery of one infeasible-floor solver failure. A 20-member ensemble showed pooled error ranking (ρ=0.894) but weak in-domain ranking (ρ=0.164); a temperature–distance heuristic was stronger (ρ=0.914), and nominal 90% conformal coverage fell from 88.0% in-domain to 0.0% under shift. Controlled flashes were 2.77× slower than NRTL. A hard thermodynamic structure is therefore valuable within neural modeling, but classical dominance, shift sensitivity, and absent molecular data bound the claim.",
  "abstract_shown": true,
  "affiliations": [
    "Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, Chile",
    "Facultad de Ingeniería, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, Chile",
    "Instituto de Investigaciones Mineras (IIM), Facultad de Ingeniería, Universidad Nacional de San Juan, San Juan 5400, Argentina",
    "Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Juan 5400, Argentina"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Pedro Robles",
      "orcid": "0000-0002-8312-7554"
    },
    {
      "affs": [
        1
      ],
      "name": "Luis Rios-Colque",
      "orcid": "0009-0007-3408-9233"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Vanesa Bazan",
      "orcid": "0000-0001-5766-6004"
    },
    {
      "affs": [
        1
      ],
      "name": "Luis Rojas-Valdivia",
      "orcid": "0009-0005-8935-7595"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5zaxe299h1pj1.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27198767",
  "fulltext_url": "https://europepmc.org/articles/PMC13665268",
  "id": "5zaxe299h1pj1",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://www.mdpi.com/1422-0067/27/19/8767",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2204|Phase Equilibria and Thermodynamics"
    ]
  ],
  "openalex": "W7214960820",
  "pdf_url": "https://europepmc.org/articles/PMC13665268?pdf=render",
  "pmcid": "PMC13665268",
  "pmid": "42884009",
  "published": "2026-09-30",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/5zaxe299h1pj1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:55Z",
      "native_id": "10.3390/ijms27198767",
      "source": "crossref",
      "url": "https://www.mdpi.com/1422-0067/27/19/8767"
    },
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42884009",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42884009"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "issn:1422-0067",
      "subfield": "general"
    }
  ],
  "title": "Structure-Preserving Excess Gibbs Learning for Multicomponent Phase Equilibria: A Falsifiable and Uncertainty-Aware Synthetic Study",
  "title_text": "Structure-Preserving Excess Gibbs Learning for Multicomponent Phase Equilibria: A Falsifiable and Uncertainty-Aware Synthetic Study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/5zaxe299h1pj1",
  "venue": "International Journal of Molecular Sciences",
  "versions": []
}