{
  "abstract": "In first-principles design of solid solutions and disordered materials, exhaustive evaluation of all possible substitutional configurations is often impractical because the number of site occupations increases exponentially. Here, we develop a framework for pre-screening mixed-crystal configurations using annealing methods, where the Ewald electrostatic energy is used as the objective function. Substitutional occupations are represented by binary variables, allowing the Ewald energy to be mapped onto an Ising-type Hamiltonian and the search for low-energy configurations to be formulated as a combinatorial optimization problem. We implement this formulation using simulated annealing (SA) and quantum annealing (QA), and benchmark their performance against exhaustive search. For the small-scale system CaYAlO$_4$, SA achieved a speed-up of about 30 times, while QA achieved a speed-up of more than 100 times; both methods identified all lowest-energy configurations. For the medium-scale system $β$-KSbF$_4$ and the large-scale Ba-doped SiAlON system, SA achieved speed-ups of about 200--300 times while stably identifying the lowest-energy structures. In contrast, QA showed limited speed-up for the medium-scale $β$-KSbF$_4$ system and missed some low-energy configurations, likely due to chain breaks. These results indicate that, among the approaches examined here, SA showed comparatively robust performance for Ewald-energy-based configuration search in mixed crystals. The proposed formulation can be implemented using publicly available libraries and provides a practical route for accelerating candidate-structure generation before first-principles calculations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.24557",
  "author_total": 4,
  "authors": [
    {
      "name": "Tack Saquai"
    },
    {
      "name": "Kenta Hongo"
    },
    {
      "name": "Ryo Maezono"
    },
    {
      "name": "Tom Ichibha"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ch2w054qvavsp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.24557",
  "fulltext_url": null,
  "id": "ch2w054qvavsp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.24557",
  "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": "W7162457507",
  "pdf_url": "https://arxiv.org/pdf/2605.24557",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ch2w054qvavsp.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:58Z",
      "native_id": "2605.24557",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.24557"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Application and Performance Assessment of Annealing Methods for Electrostatic-Energy-Based Configuration Search in Mixed Crystals",
  "title_text": "Application and Performance Assessment of Annealing Methods for Electrostatic-Energy-Based Configuration Search in Mixed Crystals",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:17Z",
  "url": "https://research.draconitic.com/paper/ch2w054qvavsp",
  "venue": "arXiv",
  "versions": []
}