{
  "abstract": "Metal-organic frameworks (MOFs) are prime CO 2 -capture candidates, but grand canonical Monte Carlo (GCMC) screening is costly when full isotherms, not single uptakes, are required; machine learning surrogates rarely resolve the continuous pressure response. We report a physics-informed pressure-aware multi-task graph neural network (PI-MGNN) to predict CO 2 uptake at arbitrary pressure and reconstruct isotherms from structure. From ~130,000 candidates, PCA and K-means select 5,023 representative MOFs, each simulated by GCMC at five pressures spanning 0.1-5.0 MPa, enforcing a continuous pressure response. Crystal graphs are processed by decoupled geometric and electrostatic graph-attention branches with auxiliary supervision, while a Gaussian-expanded pressure query is fused by cross-attention. PI-MGNN attains R 2 = 0.977 and MAE = 0.291 mol kg -1 ; isotherm MAEs are 0.428/0.312/0.330 mol kg -1 for high/medium/low-uptake MOFs, and cross-attention improves accuracy by 6.3%. Attention weights recover the pressure-resolved physics, enabling accurate and interpretable high-throughput screening.",
  "abstract_shown": true,
  "affiliations": [
    "School of Chemical Engineering, Ocean and Life Sciences",
    "Dalian University of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Li Linfeng"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Xingshuo Fan"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Ning Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9j2tx46k4gq2f.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010295/v1",
  "fulltext_url": null,
  "id": "9j2tx46k4gq2f",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010295/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7221573067",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010295/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/9j2tx46k4gq2f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15010295",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010295/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Prediction of MOF Adsorption Isotherms and High-Throughput Screening Using a Physics-Informed Membranes Graph Neural Network",
  "title_text": "Prediction of MOF Adsorption Isotherms and High-Throughput Screening Using a Physics-Informed Membranes Graph Neural Network",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/9j2tx46k4gq2f",
  "venue": "ChemRxiv",
  "versions": []
}