There are two hyperparameters in the low rank model: (1) k ∈ {0, 1, ..., min(M, P )}, the dimensionality of the vector space containing the latent representations of the materials and properties and (2) λ ∈ [0, ∞), the regularization parameter that trades off prediction accuracy on the training data and the complexity of the latent vector representations.