This repository comprises scripts used in the research paper titled 'Sequential Closed-Loop Bayesian Optimization as a Guide for Organic Molecular Metallophotocatalyst Formulations Discovery'.
The Bayesian Optimization instance BayesOptimizer, available in
src.BayesOpt.optimizer, is employed for the optimization of organic
catalysts and the reaction conditions.
The Gaussian processes instance GaussianProcessRegressor, created in
src.BayesOpt.GPR, is used as the surrogate model of the optimization
of reaction conditions.
The Jupyter notebooks in workflows are the original record of the optimization
of CNPs and reaction conditions. The kernel matrix of designed reaction
conditions need to be built before running the optimization.
| Function | Description |
|---|---|
src.data_pretreatment.ks_selection |
The Kennard-Stone algorithm used for selecting represent subset. |
src.data_pretreatment.cal_fingerprints |
Calculating the Fingerprints of given chemical SMILES. |
src.BayesOpt.BayesOptimizer.ask |
Query one point at which objective should be evaluated. |
src.BayesOpt.BayesOptimizer.parallel_ask |
Query several points at which objective should be evaluated. |
src.BayesOpt.BayesOptimizer.tell |
Recording evaluated points of the objective function. |
src.BayesOpt.acquisition_function |
Computing the acquisition function. |
src.BayesOpt.GaussianProcessRegressor.fit |
Fit Gaussian process regression model. |
src.BayesOpt.GaussianProcessRegressor.predict |
Predict using the Gaussian process regression model. |
Yu Che