{
  "abstract": "Hydrogen is a promising energy carrier for decarbonized technologies, but reversible on-board storage remains challenging because efficient uptake and release under near-ambient conditions require a narrow adsorption-energy window. Here, we combine high-throughput electronic-structure calculations with explainable machine learning to uncover the governing factors of molecular hydrogen adsorption on single-atom-doped TiO 2 nanoparticles. Screening 30 dopants across perpendicular and parallel H 2 adsorption geometries reveals substantial variation in adsorption strength and clear configuration-dependent behavior. From a broad descriptor space, a three-stage feature-selection strategy identifies eight robust predictors, which are further reduced through symbolic regression to an analytical and interpretable model. The resulting descriptor space is governed by H–H activation, dopant–H 2 distance, and -electron rearrangement. Adsorption within the target window is associated with balanced -donation and -back-donation, with Group 4 and 5 transition metals emerging as the most robust candidates across geometries. Combined with Langmuir-based desorption estimates, these results provide a mechanism-based screening rule for reversible hydrogen storage on doped TiO 2 .",
  "abstract_shown": true,
  "affiliations": [
    "Hamad Bin Khalifa University, College of Science and Engineering, Doha, Qatar",
    "Texas A&M University, Department of Electrical and Computer Engineering, College Station, Texas, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mustafa Kurban",
      "orcid": "0000-0002-7263-0234"
    },
    {
      "affs": [
        1
      ],
      "name": "Can Polat",
      "orcid": "0000-0002-1458-302X"
    },
    {
      "affs": [
        1
      ],
      "name": "Erchin Serpedin",
      "orcid": "0000-0001-9069-770X"
    },
    {
      "affs": [
        0
      ],
      "name": "Hasan Kurban",
      "orcid": "0000-0003-3142-2866"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8s6br3h9m27fn.bib",
  "cited_by": 12,
  "doi": "10.1016/j.matdes.2026.116401",
  "fulltext_url": null,
  "id": "8s6br3h9m27fn",
  "issns": [
    "0264-1275",
    "1873-4197"
  ],
  "landing_url": "https://doi.org/10.1016/j.matdes.2026.116401",
  "language": "en",
  "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|Hydrogen Storage and Materials"
    ]
  ],
  "openalex": "W7164576652",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-12",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/8s6br3h9m27fn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:27:00Z",
      "native_id": "W7164576652",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.matdes.2026.116401"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Hydrogen Storage and Materials",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Explainable machine learning reveals orbital design rules for reversible H2 adsorption on single-atom-doped TiO2 nanoparticles",
  "title_text": "Explainable machine learning reveals orbital design rules for reversible H2 adsorption on single-atom-doped TiO2 nanoparticles",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:22Z",
  "url": "https://research.draconitic.com/paper/8s6br3h9m27fn",
  "venue": "Materials & Design",
  "versions": []
}