{
  "abstract": "Advancing the design of polymeric membranes through material screening and discovery offers a powerful strategy to guide future membrane fabrication tailored to a wide range of separation challenges. Traditional trial-and-error methods often fall short in navigating this large monomer and process-design space, whereas machine learning-assisted inverse design (MLAID) can provide an extensible workflow for translating literature-derived membrane knowledge into ranked experimental candidates. Building on prior inverse-design strategies that combine machine learning (ML) with Bayesian optimization (BO), this study applies MLAID to prioritize membrane candidates within a target-defined nanofiltration (NF) design space. MLAID models are trained with membrane fabrication data, and their underlying patterns are interpreted through Shapley additive explanations (SHAP). SHAP was used to investigate model-attribution patterns, and BO was used to prioritize monomer and fabrication-condition combinations for producing polyamide membranes with targeted permeability and selectivity. The MLAID framework prioritized candidate formulations across target salt-water permeabilities of 4 to 12 LMH/bar (L m–2 h–1 bar–1), with Na2SO4 rejection used as the experimental benchmark. Fabrication and testing served as the experimental decision gate for evaluating the model-prioritized candidates. Overall, this study demonstrates how ML/BO-guided prioritization can accelerate identification of promising monomers and fabrication conditions within an encoded design space, while providing a workflow backbone that can expand as richer target-specific data sets, descriptors, and validation become available.",
  "abstract_shown": true,
  "affiliations": [
    "School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nohyeong Jeong"
    },
    {
      "affs": [
        0
      ],
      "name": "Elif Demirel"
    },
    {
      "affs": [
        0
      ],
      "name": "Changyoon Jun",
      "orcid": "0009-0007-5593-6032"
    },
    {
      "affs": [
        0
      ],
      "name": "Yongsheng Chen",
      "orcid": "0000-0002-9519-2302"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/24y938y7ycmyt.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.est.5c15241",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13738799/",
  "id": "24y938y7ycmyt",
  "issns": [
    "0013-936X",
    "1520-5851"
  ],
  "landing_url": "https://doi.org/10.1021/acs.est.5c15241",
  "language": "en",
  "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": [
    [
      "heading",
      "Article"
    ],
    [
      "heading",
      "Physico-Chemical Treatment and Resource Recovery"
    ],
    [
      "openalex",
      "2312|Membrane Separation Technologies"
    ]
  ],
  "openalex": "W7214756776",
  "pdf_url": "https://pubs.acs.org/esthag/article-pdf/doi/10.1021/acs.est.5c15241/68706049/acs.est.5c15241.pdf",
  "pmcid": "PMC13738799",
  "pmid": "42975444",
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/24y938y7ycmyt.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:23:43Z",
      "native_id": "PMC13738799",
      "source": "pmc",
      "url": "https://doi.org/10.1021/acs.est.5c15241"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2312|Membrane Separation Technologies",
      "subfield": "hydrology-soils"
    }
  ],
  "title": "Learning the Path to Ion Separation: A Proof-of-Concept for Machine Learning-Assisted Inverse Design (MLAID) of Nanofiltration Membranes",
  "title_text": "Learning the Path to Ion Separation: A Proof-of-Concept for Machine Learning-Assisted Inverse Design (MLAID) of Nanofiltration Membranes",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:23:43Z",
  "url": "https://research.draconitic.com/paper/24y938y7ycmyt",
  "venue": "Environmental Science & Technology",
  "versions": []
}