# Kinetic MonteCarlo–Ising Machine Optimizationfor Atomistic Inverse Design of Solid Electrolytes

https://mdr.nims.go.jp/datasets/844dc94a-ca6d-44ef-8440-7c27ff5ce54d

## File

- [acs.jpclett.6c02157.pdf](https://mdr.nims.go.jp/filesets/3b4593f0-5651-455c-b299-7450a4b70db7/download) ([Detail](https://mdr.nims.go.jp/filesets/3b4593f0-5651-455c-b299-7450a4b70db7.md))

## Id

844dc94a-ca6d-44ef-8440-7c27ff5ce54d

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-11-01T01:04:52.289027Z

## Updated at

2026-11-02T01:18:46.192391Z

## Published at

2026-11-02T03:33:17.532638Z

## Doi



## First published url

https://doi.org/10.1021/acs.jpclett.6c02157

## Date published

2026-09-17

## Recorded date published

2026-9-17

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: |-
    Kinetic Monte
    Carlo–Ising Machine Optimization
    for Atomistic Inverse Design of Solid Electrolytes
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Ai Koizumi
  role: author
  orcid: https://orcid.org/0000-0003-4609-4586
- name: Tomofumi Tada
  role: author
  orcid: https://orcid.org/0000-0003-3093-3779
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: Ionic conductivity
  schema: not_defined
- subject: Optimization
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: |-
    The Journal of Physical
    Chemistry Letters
  issn: '19487185'
  volume: '17'
  issue: '37'
  start_page: 10758
  end_page: 10762

## Conference



## Related item



## Funding

- identifier: JPMJCR2234
  funder_name: Japan Science and Technology Agency
- identifier: JP24H02203
  funder_name: Japan Society for the Promotion of Science

## Instrument



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## Specimen



## Chemical composition



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## Fileset

- id: 3b4593f0-5651-455c-b299-7450a4b70db7
  filename: acs.jpclett.6c02157.pdf
  content_type: application/pdf
  size: 4878457
  md5: 0ef2f2026fbc3d1e21ea83d78aace8d9

## Thumbnail

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filename: acs.jpclett.6c02157.pdf