{
  "abstract": "Predicting optoelectronic response across thermodynamic state space requires coupling finite-temperature nuclear dynamics to electronic structure at scales where direct first-principles calculations are impractical. Machine-learning force fields and Hamiltonian-learning models provide scalable predictions, but integrating them into reliable and interpretable workflows remains challenging. Here, we introduce FLOW-OTTER, a modular, model-agnostic framework that automates molecular dynamics, Hamiltonian prediction, observable extraction, reliability assessment, and Hamiltonian-level interpretation. We demonstrate FLOW-OTTER in halide perovskites, soft semiconductors whose anharmonic fluctuations strongly modulate electronic response. Using FLOW-OTTER, we show that independently learned nuclear and electronic models remain predictive when composed end-to-end, reproducing experimental temperature- and pressure-dependent band-gap trends using models trained only on first-principles targets at zero pressure. By resolving the nonlinear evolution of Pb-$s$/Br-$p$ antibonding at the valence-band maximum, it identifies the microscopic origin of the asymmetric pressure response. FLOW-OTTER thus establishes Hamiltonian learning as a general route from thermodynamic trajectories to experimentally grounded optoelectronic mechanisms.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.29361",
  "author_total": 8,
  "authors": [
    {
      "name": "Frederik Vonhoff"
    },
    {
      "name": "Jesper R. Pedersen"
    },
    {
      "name": "Frederico P. Delgado"
    },
    {
      "name": "Martin Schwade"
    },
    {
      "name": "Peter Beck"
    },
    {
      "name": "Jonas A. Oldenstaedt"
    },
    {
      "name": "Ivano E. Castelli"
    },
    {
      "name": "David A. Egger"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cm6evkh87vs6f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.29361",
  "fulltext_url": null,
  "id": "cm6evkh87vs6f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.29361",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214311843",
  "pdf_url": "https://arxiv.org/pdf/2609.29361",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cm6evkh87vs6f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2609.29361",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.29361"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Hamiltonian learning reveals optoelectronic mechanisms across thermodynamic state space in soft semiconductors",
  "title_text": "Hamiltonian learning reveals optoelectronic mechanisms across thermodynamic state space in soft semiconductors",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/cm6evkh87vs6f",
  "venue": "arXiv",
  "versions": []
}