For BO, we employ ( ) a Gaussian process (GP) with the Matérn kernel (ν = 2.5) [ ] as our surrogate model and ( ) the expected improvement (EI) in eqn. as our acquisition function. To initialize the GP for BO, we first randomly select ten COFs from the database and evaluate their methane deliverable capacity. The GP surrogate model is then trained on {(x i , y i )} 10 i=1 , which count towards the number of evaluations when we report the search-efficiency of BO. At each iteration of BO, we fit a new GP to all past observations {(x i , y i )}, which includes choosing the hyperparameters of the Matérn kernel (length-scale and signal variance) by maximizing the marginal likelihood of the data under the GP. We implemented our BO procedure in the BoTorch library [ ].