{
  "abstract": "Designing polymeric membranes for diverse gas-separation applications remains a critical challenge. Machine learning (ML) models can significantly accelerate this effort by capturing complex relationships between polymer-gas pairs and enabling permeability prediction over an extended chemical space of gases and polymers. In this work, we present a novel ML framework, the gas-conditioned message passing neural network (GC-MPNN), that jointly combines gas descriptors and polymer chemistry in a fused neural network model. Polymers are represented at the monomer level and encoded as molecular graphs, where node and edge information is propagated through message-passing layers. Six gas species (He, H2, N2, O2, CH4, and CO2) are represented using multiple descriptor sets spanning thermodynamic, kinetic, and electrostatic properties, along with one-hot encodings and combined feature sets that include kinetic plus thermodynamic descriptors and the full physicochemical descriptor set. For each descriptor combination, model hyperparameters are optimized using Bayesian optimization within a leave-one-gas-out (LOGO) validation framework. Test performance is then evaluated on the gas species excluded entirely from the corresponding training fold (out-of-training set) and compared across descriptor sets. For most gas species, the model achieves strong predictive performance (R2 ∼ 0.86–0.96), while CO2 remains more challenging in terms of absolute prediction accuracy (R2 ∼ 0.44). Active learning sampling further improves model performance by adding a few labeled samples from the test gas species into the training set. Among the three strategies tested, deep ensemble-based sampling and random sampling show strong improvement, with R2 reaching approximately ∼ 0.95 for all gas species, while evidential regression shows less consistent improvement. The model is further evaluated on a smaller polymer-gas dataset containing hydrocarbon species (C2H4, C2H6, C3H6, C3H8, and n-C4H10) using the GC-MPNN trained only on the six base gases, achieving moderate to strong Pearson correlations (r ∼ 0.77–0.89) for these out-of-training gases. Overall, this work presents a machine learning model that can learn transferable polymer-gas relationships from known gas species and extend them to previously unseen gases.",
  "abstract_shown": true,
  "affiliations": [
    "McKetta Department of Chemical Engineering",
    "University of Texas at Austin",
    "Saudi Aramco"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Krishnendu Mukherjee",
      "orcid": "0000-0002-7727-1461"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zidan Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Mohammed Alshammasi"
    },
    {
      "affs": [
        2
      ],
      "name": "Mohammed G. Hashim"
    },
    {
      "affs": [
        2
      ],
      "name": "Hussain H. Naji"
    },
    {
      "affs": [
        2
      ],
      "name": "Zainab A. Aithan"
    },
    {
      "affs": [
        2
      ],
      "name": "Jihad A. Badra"
    },
    {
      "affs": [
        2
      ],
      "name": "Jalal Yagoubi"
    },
    {
      "affs": [
        2
      ],
      "name": "Hussain B. Tuwailib"
    },
    {
      "affs": [
        2
      ],
      "name": "Ali Hayek"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Venkat Ganesan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f8nf4aqh51wg6.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010381/v1",
  "fulltext_url": null,
  "id": "f8nf4aqh51wg6",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010381/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2210|Membrane Separation and Gas Transport"
    ]
  ],
  "openalex": "W7221728241",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010381/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/f8nf4aqh51wg6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010381",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010381/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Gas-Conditioned Message Passing Graph Neural Network for Permeability Prediction in Polymeric Membranes",
  "title_text": "Gas-Conditioned Message Passing Graph Neural Network for Permeability Prediction in Polymeric Membranes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/f8nf4aqh51wg6",
  "venue": "ChemRxiv",
  "versions": []
}