Python code to reproduce all plots in:

❝Bayesian optimization of nanoporous materials❞ A. Deshwal, C. Simon, J. R. Doppa. ChemRxiv. (2021) DOI

the Python 3 libraries required for the project are in requirements.txt. use Jupyter Notebook or Jupyter Lab to run Python 3 in the *.ipynb.

our paper relies on data from Mercado et al. here. visit Materials Cloud to download and untar properties.tgz. place properties.csv in the main directory.

run the code in the Jupyter Notebook prepare_Xy.ipynb to prepare the data and write inputs_and_outputs.pkl to be read in by other Notebooks.

run the following Jupyter Notebooks, which will write search results to .pkl files.

• random_search.ipynb for random search
• evol_search.ipynb for evolutionary search (CMA-ES)
• random_forest_run.ipynb for one-shot supervised machine learning (via random forests). run twice, one with the flag diversify_training = True, the other with diversify_training = False.
• BO_run.ipynb for Bayesian optimization. run three times, with which_acquisition set to "EI", "max y_hat", and max sigma.

each .ipynb can be run on a desktop computer. the BO code takes the longest, at ~10 min per run.

BoTorch libraries.

finally, run viz.ipynb to read in the *.pkl files and visualize the results.

see synthetic_example.ipynb for the toy GP plots in the paper.