Bayesian optimization package: PHYSBO

TL;DR

PHYSBO is a Python library designed for fast, scalable Bayesian optimization tailored for basic sciences, enabling efficient experiment and simulation input selection for improved outcomes.

Contribution

It introduces a scalable Bayesian optimization package specifically optimized for physics and materials science applications, supporting single and multi-objective problems.

Findings

Supports both single and multi-objective optimization

Allows interactive proposal generation for experiments

Provides scalable and fast Bayesian optimization

Abstract

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.