{
  "abstract": "Understanding the relationship between molecular structures and hydrogen adsorption performance is essential for the rational screening of ester oil impregnating agents for high-voltage capacitor applications. Herein, an interpretable machine learning-based quantitative structure–property relationship (QSPR) framework was developed to predict hydrogen adsorption performance of ester oils. A data set of 56 ester molecules was established through experimental characterization, and robust molecular descriptors were identified from a high-dimensional descriptor space using a bootstrap-assisted least absolute shrinkage and selection operator (LASSO) stability selection strategy. Multiple machine learning models were evaluated, with the optimized XGBoost model achieving excellent predictive performance (R2 = 0.940, RMSE = 10.316, and MAE = 8.707). Explainable analyses based on SHapley Additive exPlanations (SHAP) and partial dependence plots revealed key molecular features and nonlinear structure–property relationships governing hydrogen adsorption behavior. This study provides an interpretable data-driven strategy for screening ester-based functional liquids and offers molecular insights for optimizing capacitor impregnating materials.",
  "abstract_shown": true,
  "affiliations": [
    "Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences , , ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiaoyu Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Chao Ju"
    },
    {
      "affs": [
        0
      ],
      "name": "Huaigang Su"
    },
    {
      "affs": [
        0
      ],
      "name": "Qin Zhao",
      "orcid": "0000-0002-2819-6776"
    },
    {
      "affs": [
        0
      ],
      "name": "Haichao Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Qilong Zhao",
      "orcid": "0000-0003-2812-6505"
    },
    {
      "affs": [
        0
      ],
      "name": "Wenjing Lou",
      "orcid": "0000-0002-0509-7505"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4n29q0xdda4mc.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c11253",
  "fulltext_url": null,
  "id": "4n29q0xdda4mc",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c11253/5456910/Interpretable-Machine-Learning-Based-QSPR-Modeling",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7223355602",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c11253",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/4n29q0xdda4mc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:40Z",
      "native_id": "10.1021/acsomega.6c11253",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c11253/5456910/Interpretable-Machine-Learning-Based-QSPR-Modeling"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Interpretable Machine Learning-Based QSPR Modeling for Predicting Hydrogen Adsorption Performance of Ester Oil Impregnating Agents",
  "title_text": "Interpretable Machine Learning-Based QSPR Modeling for Predicting Hydrogen Adsorption Performance of Ester Oil Impregnating Agents",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:40Z",
  "url": "https://research.draconitic.com/paper/4n29q0xdda4mc",
  "venue": "ACS Omega",
  "versions": []
}