PHYSBO Gallery

Overview

PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application in the basic sciences such as physics and materials science. Bayesian optimization is used to select an appropriate input for experiments/simulations from candidate inputs listed in advance in order to obtain better output values with the help of machine learning prediction. PHYSBO can be used to find better solutions for both single and multi-objective optimization problems. At each cycle in the Bayesian optimization, a single proposal or multiple proposals can be obtained for the next experiments/simulations. These proposals can be obtained interactively for use in experiments. PHYSBO is available at https://github.com/issp-center-dev/PHYSBO. This repository provides the sample data including input and output data for PHYSBO.

URL: https://isspns-gitlab.issp.u-tokyo.ac.jp/physbo-dev/physbo-gallery
DOI: 10.1016/j.cpc.2022.108405
Citations: 99 (OpenAlex, as of 2026-10-05)
Authors: Ryo Tamura, Yuichi Motoyama, Kazuyoshi Yoshimi, Kei Terayama, Tsuyoshi Ueno, Koji Tsuda
License: Documents: CC BY 4.0, Codes: GNU General Public License v3.0 or later
Keywords: Bayesian optimizationGaussian process regressionrandom feature mapsThompson samplinghyperparameter optimizationmulti-objective optimizationscalable optimizationphysics experiments (keywords of the paper, from OpenAlex)
Category: Machine learning
References: Computer Physics Communications Volume 278, September 2022, 108405
Related software: PHYSBO (Links to: PASUMS software catalog)
Contact: physbo-dev__at__issp.u-tokyo.ac.jp (Please replace __at__ with @.)

Paper and citation

If you use this data, please cite the following paper.

Yuichi Motoyama et al., "Bayesian optimization package: PHYSBO", Computer Physics Communications 278, 108405 (2022). https://doi.org/10.1016/j.cpc.2022.108405

Read the paper (open access) ↗

Show BibTeX

@article{Motoyama_2022, title={Bayesian optimization package: PHYSBO}, volume={278}, ISSN={0010-4655}, url={http://dx.doi.org/10.1016/j.cpc.2022.108405}, DOI={10.1016/j.cpc.2022.108405}, journal={Computer Physics Communications}, publisher={Elsevier BV}, author={Motoyama, Yuichi and Tamura, Ryo and Yoshimi, Kazuyoshi and Terayama, Kei and Ueno, Tsuyoshi and Tsuda, Koji}, year={2022}, month=Sept, pages={108405} }

Bibliographic details and the open-access link are from OpenAlex; the BibTeX is from doi.org (as registered by the publisher).

Links

💻 Open repository 📦 Download ZIP

README

About

PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application in the basic sciences such as physics and materials science. Bayesian optimization is used to select an appropriate input for experiments/simulations from candidate inputs listed in advance in order to obtain better output values with the help of machine learning prediction. PHYSBO can be used to find better solutions for both single and multi-objective optimization problems. At each cycle in the Bayesian optimization, a single proposal or multiple proposals can be obtained for the next experiments/simulations. These proposals can be obtained interactively for use in experiments. PHYSBO is available at https://github.com/issp-center-dev/PHYSBO. This repository provides the sample data including input and output data for PHYSBO.

License

Documents: CC BY 4.0
Source codes:
- tutorial: Mozilla Public License Version 2.0
- tools/physbo_exe: Mozilla Public License Version 2.0
- data/paper
  - cpc_2022: GNU General Public License v3.0 or later
  - stam_2025: MIT License
  - v3_2026: Mozilla Public License Version 2.0

We hope that you cite the reference when you publish the results using PHYSBO, our data or codes:

Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi, Kei Terayama, Tsuyoshi Ueno, Koji Tsuda, "Bayesian optimization package: PHYSBO", Computer Physics Comunications 278, 108405(2022).

Bibtex:

@article{MOTOYAMA2022108405,
title = {Bayesian optimization package: PHYSBO},
journal = {Computer Physics Communications},
volume = {278},
pages = {108405},
year = {2022},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2022.108405},
url = {https://www.sciencedirect.com/science/article/pii/S0010465522001242},
author = {Yuichi Motoyama and Ryo Tamura and Kazuyoshi Yoshimi and Kei Terayama and Tsuyoshi Ueno and Koji Tsuda}
}

Folder structures

LICENSE
README.md
data
- paper
  - cpc_2022
  - stam_2025
  - v3_2026
- tutorial
  - 01.basic
  - 02.Guassian_process
  - 03.interactive_mode
  - ...
tools
- physbo_exe
  - physbo.exe.zip
  - PHYSBO_exe_manual_en.md
  - PHYSBO_exe_manual_prev.md
  - candidates.csv

Contact

physbo-dev__at__issp.u-tokyo.ac.jp

please replace __at__ by @.

Source: https://isspns-gitlab.issp.u-tokyo.ac.jp/physbo-dev/physbo-gallery README

Activity

UpdatedLast updated: 2026-09-29 (5 days ago)