Our material recommendation system deviates from previous data-driven approaches to predict properties of NPMs by: (i) as a latent variable model, embedding materials into a latent space, negating the need for explicitly hand-crafted features of the NPMs, and (ii) performing multi-task prediction and transferring knowledge between tasks while handling missing values in the target vectors associated with the NPMs. N.b. recommendation systems have been built for use in the chemical sciences to impute missing gas permeabilities in polymers , antiviral activities of molecules , and stabilities of inorganic materials .