{
  "abstract": "Machine learning can accelerate thermoelectric materials discovery, yet most models remain “black boxes” lacking physical insight. We present an interpretable framework coupling Extra Trees regression with SHAP analysis to predict electrical conductivity (EC), Seebeck coefficient (SC), and thermal conductivity (TC) across 3879 multicomponent compositions. Physics-inspired feature engineering expands descriptors to 73–84 per target via polynomial lattice-distortion terms, cross-interactions, and compositional disorder statistics. Random-split test-set values reach 0.961, 0.965, and 0.958 for EC, SC, and TC, respectively, surpassing a lightweight deep neural network. Composition-wise splitting reduces to 0.822, 0.871, and 0.876, indicating limited generalisation to unseen compositions. SHAP analysis reveals property-specific associations: elemental descriptors dominate all targets, lattice-distortion terms influence EC, and heavy-element features affect TC. As a consistency check, a ZT-proxy computed from the independently predicted transport properties agrees reasonably well with experimental values on the test set ( = 0.84); it is intended as a screening aid rather than a verified figure of merit. This interpretable framework supports screening and prioritisation backed by, not replacing, experimental validation.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Mechanical, Electrical and Computer Engineering, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, 70000, Viet Nam",
    "Laboratory for Computational Physics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, 70000, Viet Nam",
    "Department of Materials and Nanotechnology, SINTEF Industry, Trondheim, NO-7034, Norway",
    "Porelab, Department of Chemistry and Biomedical Science, Norwegian University of Science and Technology, NTNU, Trondheim, NO-7491, Norway"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Thi Huynh Ho",
      "orcid": "0000-0003-0510-5605"
    },
    {
      "affs": [
        2
      ],
      "name": "Nguyen-Hieu Hoang",
      "orcid": "0000-0001-7337-2903"
    },
    {
      "affs": [
        3
      ],
      "name": "Thuat Thanh Trinh",
      "orcid": "0000-0002-1721-6786"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1a8g47wqf2bs6.bib",
  "cited_by": 5,
  "doi": "10.1016/j.matdes.2026.116533",
  "fulltext_url": null,
  "id": "1a8g47wqf2bs6",
  "issns": [
    "0264-1275",
    "1873-4197"
  ],
  "landing_url": "https://doi.org/10.1016/j.matdes.2026.116533",
  "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|Machine Learning in Materials Science"
    ]
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  "openalex": "W7166649968",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-30",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/1a8g47wqf2bs6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:27:01Z",
      "native_id": "W7166649968",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.matdes.2026.116533"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Physics-inspired feature engineering and interpretable machine learning for thermoelectric properties of multicomponent materials",
  "title_text": "Physics-inspired feature engineering and interpretable machine learning for thermoelectric properties of multicomponent materials",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:45Z",
  "url": "https://research.draconitic.com/paper/1a8g47wqf2bs6",
  "venue": "Materials & Design",
  "versions": []
}