{
  "abstract": "Maximizing ionic conductivity remains a fundamental challenge in the atomistic design of solid electrolytes. To this end, we present a framework that combines kinetic Monte Carlo (KMC) and factorization machine with quadratic-optimization annealing (FMQA), an Ising-machine-based black-box optimization algorithm for large-scale combinatorial problems. KMC evaluates the ionic conductivity for a given dopant configuration, whereas FMQA iteratively learns a surrogate model from a small configuration-conductivity data set and proposes configurations expected to maximize conductivity. To address the severe combinatorial explosion in large KMC simulation cells, we partition the configuration space for parallel optimization. As a proof of concept, we apply this KMC-FMQA framework to bulk 8 mol % yttria-stabilized zirconia, identifying a dopant configuration with an order-of-magnitude higher conductivity than that of random configurations. Combined with experiments, this framework will enable the determination of microscopic structures from measured conductivity, providing insight into the underlying transport mechanisms.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki305-0044, Japan.",
    "Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-8577, Japan.",
    "Kyushu University Platform of Inter/Transdisciplinary Energy Research, Kyushu University , 744 Motooka, Nishi-ku, Fukuoka819-0395, Japan.",
    "Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba277-8561, Japan."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ai Koizumi",
      "orcid": "0000-0003-4609-4586"
    },
    {
      "affs": [
        2
      ],
      "name": "Tomofumi Tada",
      "orcid": "0000-0003-3093-3779"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Ryo Tamura",
      "orcid": "0000-0002-0349-358X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9prvejbm2phdk.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.jpclett.6c02157",
  "fulltext_url": "https://europepmc.org/articles/PMC13592075",
  "id": "9prvejbm2phdk",
  "issns": [
    "1948-7185"
  ],
  "landing_url": "https://europepmc.org/article/MED/42915475",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7166822933",
  "pdf_url": "https://europepmc.org/articles/PMC13592075?pdf=render",
  "pmcid": "PMC13592075",
  "pmid": "42915475",
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9prvejbm2phdk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:02Z",
      "native_id": "MED:42915475",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42915475"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Kinetic Monte Carlo-Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes.",
  "title_text": "Kinetic Monte Carlo-Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:02Z",
  "url": "https://research.draconitic.com/paper/9prvejbm2phdk",
  "venue": "The journal of physical chemistry letters",
  "versions": []
}