{
  "abstract": "The discovery of stable, high-performance lead-free hybrid perovskite materials remains a major challenge for next-generation photovoltaic technologies. Here, a high-throughput computational framework integrating machine learning, deep learning, and density functional theory (DFT) calculations was adopted to accelerate the discovery and validation of lead-free hybrid perovskites. An optimized XGBoost model was firstly employed to screen approximately 180k charge-neutral perovskite candidates according to formation-energy and bandgap criteria. The resulting 48 kinds of Ge-based candidates were subsequently validated using CGCNN and UPET models, leading to the identification of (CH)4NH2GeGeCl6, C3H4NSGeGeBr6, C5NH12GeGeBr6 and C6OH13GeGeBr6 as promising photovoltaic materials, with HSE + SOC-calculated bandgaps of 1.5137, 1.7515, 1.6778 and 1.1680 eV, respectively. These results demonstrate that integrating data-driven prediction with first-principles validation provides an effective strategy for accelerating the discovery of next-generation photovoltaic materials.",
  "abstract_shown": true,
  "affiliations": [
    "Lynbrook High School 1280 Johnson Avenue San Jose CA 95129 USA",
    "College of Future Information Technology and Key Laboratory of Micro and Nano Photonic Structures (MOE), Fudan University Shanghai 200433 China",
    "School of Physics, East China Normal University Shanghai 200062 China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Carrie Song"
    },
    {
      "affs": [
        1
      ],
      "name": "R. X. Lian",
      "orcid": "0009-0000-0252-6207"
    },
    {
      "affs": [
        1
      ],
      "name": "H. Zhang",
      "orcid": "0000-0002-8201-3272"
    },
    {
      "affs": [
        2
      ],
      "name": "Q. Y. Jin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9jjg6v1wdqn4m.bib",
  "cited_by": 0,
  "doi": "10.1039/d6ra07191k",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13690253/?tool=EBI",
  "id": "9jjg6v1wdqn4m",
  "issns": [
    "2046-2069"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13690253",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Chemistry"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7223231228",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13690253/pdf/?tool=EBI",
  "pmcid": "PMC13690253",
  "pmid": "42923869",
  "published": "2026-10-22",
  "publisher": "Royal Society of Chemistry",
  "ris": "https://research.draconitic.com/paper/9jjg6v1wdqn4m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:59Z",
      "native_id": "PMC:PMC13690253",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13690253"
    },
    {
      "first_seen": "2026-11-07T12:23:38Z",
      "native_id": "PMC13690253",
      "source": "pmc",
      "url": "https://doi.org/10.1039/d6ra07191k"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Discovery of germanium-based hybrid perovskites with high photovoltaic performance with machine-learning",
  "title_text": "Discovery of germanium-based hybrid perovskites with high photovoltaic performance with machine-learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:21:07Z",
  "url": "https://research.draconitic.com/paper/9jjg6v1wdqn4m",
  "venue": "RSC advances",
  "versions": []
}