{
  "abstract": "Isotope effects govern fractionation and modulate reactivity, with applications from hydrogen energy to environmental science and catalysis, yet predicting them requires resolving small isotope-dependent free-energy differences that remain very challenging for conventional path-integral simulations in complex systems. Here we introduce iso-EPIGS, a path-integral coarse-graining framework built on a mass-differentiable neural network that reconstructs the mass- and temperature-dependent path integral centroid free-energy surface. Classical molecular dynamics on the learned surface yields rigorous isotope-resolved thermodynamics without explicit path-integral sampling. Benchmarks spanning gas, liquid, and crystalline phases, including liquid water and oxalic acid crystal, reproduce reference path-integral isotope free-energy differences, enthalpies, and lattice parameters at near classical computational cost. Crucially, iso-EPIGS trained solely on all-H and all-D isotopologues retains high accuracy for unseen partially deuterated isotopologues, demonstrating robust transferability across nuclear masses. Iso-EPIGS makes accurate isotope-effect simulations feasible for complex systems and lays the groundwork for foundation models of isotope effects across chemical space.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.11325",
  "author_total": 8,
  "authors": [
    {
      "name": "Ming-Zheng Du"
    },
    {
      "name": "Shi-Yu He"
    },
    {
      "name": "Jing Shen"
    },
    {
      "name": "Ziyan Ye"
    },
    {
      "name": "Jia-Xi Zeng"
    },
    {
      "name": "Dong H. Zhang"
    },
    {
      "name": "Venkat Kapil"
    },
    {
      "name": "Wei Fang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/exjzd3v78v2wc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.11325",
  "fulltext_url": null,
  "id": "exjzd3v78v2wc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.11325",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "3107|Quantum, superfluid, helium dynamics"
    ]
  ],
  "openalex": "W7221174694",
  "pdf_url": "https://arxiv.org/pdf/2610.11325",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/exjzd3v78v2wc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "2610.11325",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.11325"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Isotope Effects at Classical Cost through Mass-Differentiable Machine Learning",
  "title_text": "Isotope Effects at Classical Cost through Mass-Differentiable Machine Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/exjzd3v78v2wc",
  "venue": "arXiv",
  "versions": []
}