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PHYSBO (optimization tools for PHYsics based on Bayesian Optimization )

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PHYSBO is a Python library for researchers mainly in the materials science field to perform fast and scalable Bayesian optimization based on COMBO (Common Bayesian Optimization). Users can search the candidate with the largest objective function value from candidates listed in advance by using machine learning prediction. PHYSBO can handle a larger amount of data compared with standard implementations such as scikit-learn.

Information
Last Update:2024/07/18

Official site

https://www.pasums.issp.u-tokyo.ac.jp/physbo/en/

License

Mozilla Public License v. 2.0

Availability
  • Python >= 3.6
  • numpy
  • scipy
Core Developers

Ryo Tamura (International Center for Materials Nanoarchitectonics, National Institute for Materials Science)
Kei Terayama (Graduate School of Medical Life Science, Yokohama City University)
Tsuyoshi Ueno (Magne-Max Capital Management Company)
Koji Tsuda(Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo.)
Yuichi Motoyama (Institute for Solid State Physics, The University of Tokyo)
Kazuyoshi Yoshimi (Institute for Solid State Physics, The University of Tokyo)
Naoki Kawashima (Institute for Solid State Physics, The University of Tokyo)
Methodology

Bayesian optimization
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Related keywords
Document

Tsuyoshi Ueno, Trevor David Rhone, Zhufeng Hou, Teruyasu Mizoguchi and Koji Tsuda,
COMBO: An Efficient Bayesian Optimization Library for Materials Science,
Materials Discovery 4, 18-21 (2016).

Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi, Kei Terayama, Tsuyoshi Ueno, Koji Tsuda,
Bayesian optimization package: PHYSBO
Comput. Phys. Commun. 278, 108405 (2022).
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