| Official site | https://www.pasums.issp.u-tokyo.ac.jp/physbo/en |
|---|---|
| Openness | ★★★ |
| License |
Mozilla Public License v. 2.0 |
| 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) |
| Availability |
Python>=3.6 |
| Related Papers |
Tsuyoshi Ueno, Trevor David Rhone, Zhufeng Hou, Teruyasu Mizoguchi and Koji Tsuda, Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi, Kei Terayama, Tsuyoshi Ueno, Koji Tsuda, “Bayesian optimization package: PHYSBO” |
| Related Sites |
MateriApps: https://ma.issp.u-tokyo.ac.jp/en/app/4996 |
PHYSBO
PHYSBO is a Python library for fast and scalable Bayesian optimization. It is useful for finding combinations of parameters (material composition, structure, process and simulation parameters, etc.) that improve the value of the objective function (material properties, etc.) with the fewest number of experiments and simulations. Users can set the parameters and the objective function and apply them to problems in a wide variety of fields. It is designed to be highly scalable, using Thompson sampling, random feature maps, 1-rank Cholesky updating, and automatic tuning of hyperparameters to allow the application of large amounts of training data.
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