# Update of PHYSBO: Improving usability and portability of Bayesian optimization for physics and materials research

https://mdr.nims.go.jp/datasets/04bec416-3f4c-4104-b5ac-5aafc24cd63e

## File

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## Id

04bec416-3f4c-4104-b5ac-5aafc24cd63e

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-11-01T01:15:30.995882Z

## Updated at

2026-11-02T01:40:13.967768Z

## Published at

2026-11-02T03:33:15.624089Z

## Doi



## First published url

https://doi.org/10.1016/j.cpc.2026.110368

## Date published

2026-08-29

## Recorded date published

2026-12

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 'Update of PHYSBO: Improving usability and portability of Bayesian optimization
    for physics and materials research'
  title_type: original
  lang: en

## Description

- description: "Bayesian optimization (BO) is widely used to accelerate physics and
    materials research, where objective function evaluations are computationally or
    experimentally expensive. While many BO frameworks focus on algorithmic efficiency,
    practical usability and portability are equally critical for sustained use in
    real research environments. PHYSBO is a Bayesian optimization library designed
    to address these needs by enabling optimization over user-defined candidate pools
    and by supporting domain-specific problem settings.\r\nThis paper presents the
    major updates introduced in PHYSBO versions 2 and 3, with a focus on improvements
    in usability, portability, and practical deployment rather than on new optimization
    algorithms. In PHYSBO version 2, the software license was changed from GPL to
    MPL to improve compatibility with a wider range of research and software ecosystems.
    Building on this revision, PHYSBO version 3 introduces a set of implementation-oriented
    updates aimed at improving usability and portability, without modifying the core
    optimization algorithms. These updates include improvements in computational performance
    and scalability, extended support for multi-objective optimization, the introduction
    of range-based policies for continuous-variable optimization, the removal of environment-dependent
    components such as tightly coupled Cython modules, and compatibility with NumPy
    2.\r\nThese improvements reduce the technical and organizational burden on users,
    enabling PHYSBO to be deployed across diverse computing environments and research
    workflows. By emphasizing portability and ease of integration while maintaining
    sufficient performance, PHYSBO version 3 is positioned as a sustainable research
    infrastructure for Bayesian optimization in physics and materials science."
  description_type: abstract
  lang: und

## Creator

- name: Yuichi Motoyama
  role: author
  orcid: https://orcid.org/0000-0001-8271-3327
- name: Kazuyoshi Yoshimi
  role: author
- name: Tatsumi Aoyama
  role: author
- name: Kei Terayama
  role: author
- name: Koji Tsuda
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Bayesian optimization
  schema: not_defined
- subject: Research software
  schema: not_defined

## Rights

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

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## Embargo



## Journal

- title: Computer Physics Communications
  issn: '00104655'
  volume: '329'
  article_number: '110368'

## Conference



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## Funding

- funder_name: University of Tokyo
- funder_name: Japan Society for the Promotion of Science
- funder_name: Ministry of Education, Culture, Sports, Science and Technology

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filename: 1-s2.0-S0010465526003504-main.pdf