In summary, Fig. illustrates that the recommendation system machine-learns a map of COFs, wherein COFs with similar adsorption properties congregate. These latent representations were learned from the observed values in an incomplete COF-adsorption-property matrix. Such a map of COFs, wherein proximity implies similarity of adsorption properties, is practically useful for: (1) lead-optimization, where we search the latent space for nearest neighbors of a lead COF with good but insufficient performance, (2) selecting diverse sets of COFs in an experimental design strategy to efficiently explore COF space, and (3) building supervised machine learning models for other ad-sorption tasks, where the latent representations can serve as feature vectors for the COFs.