The GP in eqn. 5 is designed to (i) incorporate our domain knowledge that COFs with similar pore size, surface area, composition, etc. will tend to exhibit similar Xe/Kr selectivities and (ii) learn, from the simulation data D [n] , (a) the relationship between the structural and compositional features of COFs in x and simulated Xe/Kr selectivity y and (b) through the fidelity kernel, correlations between the low-and high-fidelity simulations, allowing outcomes of low-fidelity simulations to inform us about the high-fidelity Xe/Kr selectivity we ultimately wish to maximize. Fig. , middle panel, visualizes a toy multi-fidelity GP for a onedimensional COF space: the dark lines show the mean function µ(x, ); the shaded bands highlight µ(x, )±σ(x, ), quantifying uncertainty by showing a credible interval for each predicted selectivity; and the points show the multi-fidelity data D [n] on which the toy GP is trained.