Analysis. We use this new data point to update our surrogate model of the simulations. This surrogate model is a supervised machine learning model that can, with negligible computational runtime, predict both the low-and high-fidelity simulated Xe/Kr selectivity of a COF not simulated before-and quantify uncertainty the uncertainty in this prediction. The inputs to the surrogate model for its prediction about a COF are (cheaply computed) structural and chemical features of its crystal structure. The surrogate model is trained on all labeled data-i.e., In practice, we cannot know for certain when we have recovered the optimal COF. Possible strategies to terminate the iterative MFBO search include when: (i) computational resources are exhausted, (ii) a COF with a sufficiently large high-fidelity Xe/Kr selectivity has been recovered, or (iii) a large runtime has elapsed since we last discovered a COF with an improved Xe/Kr selectivity over those COFs we have acquired thus far.