Paper

Paper is here: https://doi.org/10.1038/s41524-022-00859-8

Cite the following article to refer to this work.

@article{wo-npjcm2022,
  title = {{Bayesian} Optimization with Experimental Failure for High-Throughput Materials Growth},
  author = {Yuki K. Wakabayashi and Takuma Otsuka and Yoshiharu Krockenberger and Hiroshi Sawada and Yoshitaka Taniyasu and Hideki Yamamoto},
  journal = {npj Computational Materials},
  volume = {8},
  number = {180},
  doi = {10.1038/s41524-022-00859-8},
  year = {2022}
}

How to run

Use run_BO.py to carry out BO and display the result.

1. Example 1: method F

$ python run_BO.py --num 100 --init 5 --run 3 --obj circle executes BO until having 100 observations with 5 initial observations for 3 times to average the results.

2. Example 2: @0

$ python run_BO.py --floor 0

--floor {value} option specifies the constant value to replace failed observations. By default, floor padding trick is employed.

3. Example 3: other objective functions

$ python run_BO.py --obj hole runs experiment with the Hole function.

4. Example 4: put Gaussian observation noise

$ python run_BO.py --alpha 0.005 adds a Gaussian noise to the observation with its variance being 0.005.

5. Example 5: use binary classifier

$ python run_BO.py --binary --obj hole runs method FB.

Add --binary to enable the binary classifier. Can be combined with --floor option. For example, --binary --floor 0 runs B@0.

Software version

Codes are confirmed to run with the following libraries. Likely to be compatible with newer versions.

• python: 3.7.9
• numpy: 1.19.2
• scipy: 1.5.2
• sklearn: 1.0.2
• torch: 1.8.1
• gpytorch: 1.5.1
• matplotlib: 3.5.0

Files

• README.md: This file.
• LICENSE.md: Document of agreement for using this sample code. Read this carefully before using the code.
• run_BO.py: Script to execute BO sequence.
• BO_core.py: Implements BO class.
• obj_func.py: Implements objective functions.
• visualize.py: Contains some functions to plot optimization results.
• lhsmdu.py: Latin hypercube sampling package for acquisition function. Repository: https://dx.doi.org/10.5281/zenodo.2578780 Paper:http://dx.doi.org/10.1016%2Fj.jspi.2011.09.016