Bayesian optimization of nanoporous materials

Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate Bayesian optimization (BO) to find the optimal NPM in a library of NPMs using the fewest experiments. The two ingredients of BO are a surrogate model and an acquisition function. The surrogate model is a probabilistic model reflecting our beliefs about the NPM-structure--property relationship based on observations from past experiments. The acquisition function uses the surrogate model to score each NPM according to the utility of picking it for the next experiment, while balancing exploitation and exploration. We demonstrate BO by searching a database of covalent organic frameworks (COFs) for the COF with the highest simulated methane deliverable capacity.