# Black-box optimization technique for investigation of surface phase diagram

https://mdr.nims.go.jp/datasets/039a6c7a-8e01-4e03-a997-0ffb32151fbb

## File

- [125008_1_5.0229856.pdf](https://mdr.nims.go.jp/filesets/63d5f9e6-1f55-4b15-b968-baa7be2ae536/download) ([Detail](https://mdr.nims.go.jp/filesets/63d5f9e6-1f55-4b15-b968-baa7be2ae536.md))

## Id

039a6c7a-8e01-4e03-a997-0ffb32151fbb

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-09T05:57:44.778434Z

## Updated at

2024-12-10T07:55:25.246691Z

## Published at

2024-12-10T07:55:25.324477Z

## Doi



## First published url

https://doi.org/10.1063/5.0229856

## Date published

2024-12-01

## Recorded date published

2024-12-1

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Black-box optimization technique for investigation of surface phase diagram
  title_type: original
  lang: en

## Description

- description: Surface phase diagrams are useful in material design for understanding
    catalytic reactions and deposition processes and are usually obtained by numerical
    calculations. However, a large number of calculations are required, and a strategy
    to reduce the computation time is necessary. In this study, we proposed a black-box
    optimization strategy to investigate the surface phase diagram with the smallest
    possible number of calculations. Our method was tested to examine the phase diagram
    in which two types of adsorbates, i.e., oxygen and carbon monoxide, were adsorbed
    onto a palladium surface. In comparison with a random calculation without using
    machine learning, we confirmed that the proposed method obtained a surface phase
    diagram with a small number of calculations. In conclusion, our strategy is a
    general-purpose method that can contribute to the rapid study of various types
    of surface phase diagrams.
  description_type: abstract
  lang: und

## Creator

- name: Makoto Urushihara
  role: author
- name: Kenji Yamaguchi
  role: author
  orcid: https://orcid.org/0009-0004-6743-0299
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X

## Contact agent



## Publisher

organization: AIP Publishing

## Managing organization



## Keyword

- subject: machine learning
  schema: not_defined
- subject: surface phase diagram
  schema: not_defined

## Rights

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

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## Journal

- title: AIP Advances
  issn: '21583226'
  volume: '14'
  issue: '12'

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## Fileset

- id: 63d5f9e6-1f55-4b15-b968-baa7be2ae536
  filename: 125008_1_5.0229856.pdf
  content_type: application/pdf
  size: 5631196
  md5: 8d0aea4175851b41d31be32d0cd86de2

## Thumbnail

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filename: 125008_1_5.0229856.pdf