# Surface-Structure Search with Variable Composition and Periodicity via Machine Learning and Evolutionary Algorithms: Applications to Pt/Ge Oxidation and Au--Sn Alloying

https://mdr.nims.go.jp/datasets/c8c9d667-d954-4296-a6f3-583c66cabb8a

## File

- [STAM-2026-0082_data.zip](https://mdr.nims.go.jp/filesets/9806720b-9e41-46e0-96e5-ed050015830d/download) ([Detail](https://mdr.nims.go.jp/filesets/9806720b-9e41-46e0-96e5-ed050015830d.md))
- [Surface-structure search with variable composition and periodicity via machine learning and evolutionary algorithms  applications to Pt Ge oxidation a.pdf](https://mdr.nims.go.jp/filesets/a095dd7f-083a-4c9b-93b8-0e02f6a8686a/download) ([Detail](https://mdr.nims.go.jp/filesets/a095dd7f-083a-4c9b-93b8-0e02f6a8686a.md))

## Id

c8c9d667-d954-4296-a6f3-583c66cabb8a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-07-08T05:33:19.129104Z

## Updated at

2026-07-08T07:54:06.655981Z

## Published at

2026-07-08T09:24:57.752124Z

## Doi

https://doi.org/10.48505/nims.6387

## First published url

https://doi.org/10.1080/14686996.2026.2688059

## Date published

2026-07-07

## Recorded date published



## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: 'Surface-Structure Search with Variable Composition and Periodicity via Machine
    Learning and Evolutionary Algorithms: Applications to Pt/Ge Oxidation and Au--Sn
    Alloying'
  title_type: original
  lang: en

## Description

- description: First-principles structure prediction is essential for discovering
    functional materials; however, surface structure searches remain challenging because
    most search algorithms assume fixed in-plane periodicity and composition. Here
    we develop a global search framework that treats both two-dimensional superlattice
    periodicity and stoichiometry as dynamic variables, enabling the direct identification
    of the most stable surface structures across competing supercell shapes and compositions.
    The proposed method integrates an evolutionary algorithm with surface-specific
    variation operators and symmetry-enriched initialization, and accelerates screening
    via Bayesian optimization using atomic cluster expansion descriptors. Case studies
    on FCC Pt(111) and diamond Ge(100) surfaces yield oxygen-induced surface structures
    consistent with experimental observations, and the same framework identifies Sn
    alloying motifs on FCC Au(111) that agree with reported surface-structure trends.
    Overall, the framework delivers accurate structure prediction with substantially
    fewer high-cost first-principles evaluations and provides a general route to exploring
    complex materials landscapes – such as heterogeneous catalysis, electronics, and
    spintronics – in which coupled structural and compositional degrees of freedom
    govern functionality.
  description_type: abstract
  lang: en

## Creator

- name: F. Kuroda
  role: author
  organization: National Institute of Advanced Industrial Science and Technology (AIST)
  department: a Materials DX Research Center
- name: M. Otani
  role: author

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: Sections
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- subject: mathematics
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- subject: appendices
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## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Science and Technology of Advanced Materials
  issn: '14686996'
  volume: '27'
  article_number: '2688059'

## Conference



## Related item



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## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 9806720b-9e41-46e0-96e5-ed050015830d
  filename: STAM-2026-0082_data.zip
  content_type: application/zip
  size: 4942
  md5: 84e163ffb441bcf85cdc5ec91d5d9030
- id: a095dd7f-083a-4c9b-93b8-0e02f6a8686a
  filename: Surface-structure search with variable composition and periodicity via
    machine learning and evolutionary algorithms  applications to Pt Ge oxidation
    a.pdf
  content_type: application/pdf
  size: 1740207
  md5: 65ed41b223e209f5ee8baa9c4772f709

## Thumbnail

fileset_id: a095dd7f-083a-4c9b-93b8-0e02f6a8686a
filename: Surface-structure search with variable composition and periodicity via machine
  learning and evolutionary algorithms  applications to Pt Ge oxidation a.pdf