Our hypothesis, which would permit accurate matrix completion, is that the NPM-property matrix exhibits a low rank structure , owing to underlying structural and chemical similarities among both NPMs and gases that dictate their interactions. A low rank structure implies both NPMs and adsorption properties can be represented by low-dimensional vectors that together express the affinity between a (NPM, property) pair. These latent representations can be jointly machine-learned using the observed (NPM, property) values, then used to impute the missing (NPM, property) values .