PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization.
Bayesian optimization is a technique that can be used for complex simulations and real-world experimental tasks where the evaluation of objective function values (e.g., characteristic values) is very costly. In other words, the problem solved by Bayesian optimization is to find a parameter (e.g., material composition, structure, process and simulation parameters) with a better objective function value (e.g., material properties) in as few experiments and simulations as possible. In Bayesian optimization, the candidate parameters to be searched for are listed in advance, and the candidate with the largest objective function value is selected from among the candidates by making good use of machine learning (using Gaussian process regression) prediction. Experiments and simulations are performed on the candidates and the objective function values are evaluated. By repeating the process of selection by machine learning and evaluation by experimental simulation, we can reduce the number of times of optimization.
We would like to thank the support from “Project for advancement of software usability in materials science” by The Institute for Solid State Physics, The University of Tokyo, for development of PHYSBO.
Bayesian optimization (COMBO.exe) and efficient phase diagram construction method (PDC.exe) can be executed on Windows computers without Python or other settings.
COMBO, a Bayesian optimization package, and PDC, a phase diagramming efficiency method, are converted to executable files with minimal functionality.
By rewriting the CSV file, any problem can be solved.
Please download and use the files with the understanding that they cannot be modified.