{
  "abstract": "Singlet fission (SF) offers a promising route to surpass the Shockley-Queisser limit by converting a photoexcited singlet exciton into two triplet excitons, thereby enhancing photovoltaic energy conversion efficiency. However, realizing efficient SF process requires stringent energetic requirements among low-lying excited states that render SF molecules intrinsically rare within the vast chemical space. This extreme sparsity presents a grand challenge for molecular discovery. Due to low hit rates and trial-and-error computational waste on nonviable structures, conventional high-throughput virtual screening faces significant constraints, even when accelerated by machine learning models. Here, we establish a synergistic generative-predictive framework for the targeted inverse design of SF molecules by integrating a structure generator, a properties predictor and a multi-criteria validation workflow. By continuously coupling generative exploration with SF predictive models, the framework progressively enriches SF species and achieves a success rate of approximately 90% in generating molecules that satisfy the target SF energetic criteria. High-throughput evaluation of about 100 million generated structures with time-dependent density functional theory (TDDFT) validation of just a random 1% subset confirmed a 90.8% success rate for SF candidates. All together, we constructed an SF database of 283,559 candidates with favorable energetics of excited states and synthetic accessibility. From it, we identified a key fragment strongly associated with the requirements for SF, namely, CN([O])N(C)[O]. These findings establish an efficient route for overcoming the sparsity difficulty in SF molecular discovery and provide interpretable design principles for the development of novel excited-state functional materials.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.15136",
  "author_total": 5,
  "authors": [
    {
      "name": "Longfei Lv"
    },
    {
      "name": "Li Fu"
    },
    {
      "name": "Si Zhou"
    },
    {
      "name": "Lingzhi Zhao"
    },
    {
      "name": "Jijun Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4v2xgtpd5kvxj.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.15136",
  "fulltext_url": null,
  "id": "4v2xgtpd5kvxj",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.15136",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7213348038",
  "pdf_url": "https://arxiv.org/pdf/2609.15136",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4v2xgtpd5kvxj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2609.15136",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.15136"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Navigating Sparse Singlet Fission Chemical Space: An Intelligent Generative-Predictive Paradigm",
  "title_text": "Navigating Sparse Singlet Fission Chemical Space: An Intelligent Generative-Predictive Paradigm",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/4v2xgtpd5kvxj",
  "venue": "arXiv",
  "versions": []
}