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

Yuichi Motoyama; Kazuyoshi Yoshimi; Tatsumi Aoyama; Kei Terayama; Koji Tsuda; Ryo Tamura

doi:10.1016/j.cpc.2026.110368

ジャーナル論文 Update of PHYSBO: Improving usability and portability of Bayesian optimization for physics and materials research

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Yuichi Motoyama, Kazuyoshi Yoshimi, Tatsumi Aoyama, Kei Terayama, Koji Tsuda, Ryo Tamura. Update of PHYSBO: Improving usability and portability of Bayesian optimization for physics and materials research. Computer Physics Communications. 2026, 329 (), 110368. https://doi.org/10.1016/j.cpc.2026.110368

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(abstract)

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.
This 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.
These 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.

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