{
  "abstract": "Perovskite solar cell design involves a large and highly coupled space of material compositions, layer structures, and physical parameters, making systematic exploration of complete device architectures computationally challenging. This work introduces PervoTransformer, a transformer-based framework that represents complete device architectures as sequences and learns device-level behavior directly from this representation. A RoBERTa model is fine-tuned on SCAPS-generated device sequences spanning six perovskite families, including MAPbI3, FAPbI3, CsPbI3, and their Sn-based analogs. The model simultaneously predicts multiple performance metrics, including power conversion efficiency (PCE), short-circuit current density (Jsc), open-circuit voltage (Voc), and fill factor (FF), demonstrating the ability of transformer encoders to capture coupled, nonlinear dependencies across complete device stacks. To explore configurations beyond the training distribution, a GPT-2 model is fine-tuned using the same device-sequence representation to generate candidate architectures with new combinations of compositional and physical parameters. Generated configurations are screened for validity and uniqueness and subsequently evaluated using the fine-tuned RoBERTa model, enabling efficient exploration of under-sampled regions of the device design space. In parallel, machine-learned interatomic potentials based on the MACE framework are fine-tuned for CsSnX3 (X = Cl,Br, I) using available DFT data, enabling efficient structural relaxation and exploration of lead-free Sn-based perovskites. The resulting multiscale framework combines sequence-based transformer models for device-level design with machine-learned interatomic potentials for atomistic materials exploration, while retaining first-principles calculations for detailed physical validation. This approach provides a scalable strategy for navigating the coupled materials and device design spaces of perovskite solar cells while reducing reliance on computationally expensive simulations during initial exploration.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physics",
    "Quaid-i-Azam University Islamabad",
    "National University of Sciences & Technology Islamabad",
    "Kohsar University Murree Punjab"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Wahid Hussain",
      "orcid": "0009-0008-9520-0553"
    },
    {
      "affs": [
        2
      ],
      "name": "Ali Hamza"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Muhammad Sultan"
    },
    {
      "affs": [
        2
      ],
      "name": "Fouzia Perveen Malik"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ch17sfmeb86sz.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009607/v1",
  "fulltext_url": null,
  "id": "ch17sfmeb86sz",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009607/v1",
  "language": null,
  "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"
    ]
  ],
  "openalex": "W7214789780",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ch17sfmeb86sz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009607",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009607/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "PervoTransformer: Multi-Scale Transformer-Based Device Design and Atomistic Modeling of Perovskites",
  "title_text": "PervoTransformer: Multi-Scale Transformer-Based Device Design and Atomistic Modeling of Perovskites",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/ch17sfmeb86sz",
  "venue": "ChemRxiv",
  "versions": []
}