{
  "abstract": "Machine learning Hamiltonians use local atomic structures to determine local Hamiltonian matrix elements without carrying out self-consistent calculations. They have been used to study large systems and to carry out high-throughput screening for their electronic and optical properties. The prediction of the Hamiltonian is essentially the prediction of the electron electrostatic potential energy; however, for heterogeneous systems, this potential energy is often determined by faraway features, such as interface or surface dipoles, or even an applied voltage in a nanodevice. Thus, it cannot be determined by local atomic structures alone. In this sense, one needs to solve the electron electrostatic potential energy self-consistently using intrinsic atomic dipoles, the applied external potential energy, and polarizabilities. Fortunately, the intrinsic dipoles and polarizabilities are local properties, whereas the applied potential energy is a prescribed input. In this work, we propose DeepH-Polar, a physically informed machine learning Hamiltonian model that explicitly predicts intrinsic atomic dipoles and polarizability tensors and uses them to solve the electron electrostatic potential energy self-consistently. Benchmarks on polar GaN systems and crystalline-Si/amorphous-SiO$_2$ interfaces demonstrate improved Hamiltonian accuracy and reproduction of thickness-dependent electrostatic variations, as well as spatially nonuniform field-induced screening that models without polarization fail to capture. Our model also allows us to study nanoscale electronic devices. By iteratively solving the Hamiltonian eigenproblem and self-consistently updating free-carrier occupations and the electrostatic potential, we simulate an 8,194-atom gated semiconductor device.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36839",
  "author_total": 5,
  "authors": [
    {
      "name": "Jia-Yu Zhou"
    },
    {
      "name": "Wentao Zhang"
    },
    {
      "name": "Mouyi Weng"
    },
    {
      "name": "Zhao-Yi Yan"
    },
    {
      "name": "Lin-Wang Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4tmzjcxm740w5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36839",
  "fulltext_url": null,
  "id": "4tmzjcxm740w5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36839",
  "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",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7224096602",
  "pdf_url": "https://arxiv.org/pdf/2610.36839",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4tmzjcxm740w5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.36839",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36839"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Machine Learning Hamiltonians with Polarizations for Heterogeneous Systems",
  "title_text": "Machine Learning Hamiltonians with Polarizations for Heterogeneous Systems",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/4tmzjcxm740w5",
  "venue": "arXiv",
  "versions": []
}