To understand the behavior of BO for searching the database of COFs for the optimal COF x * , we visualize the acquisition set of COFs in feature space as BO progresses. Given that the feature space is -dimensional, we resort to principal component analysis (PCA) to [approximately] reduce the dimension of the feature space to two. I.e., we project each COF feature space onto a D reduced feature space through PCA of the data