While simple and fast to compute, the RDF is an oversimplified crystal representation for ML as three structural dimensions are compressed to one. As such, related methods have been developed to include additional information, such embedding atomic species information (partial RDF) or bond angles (many-body tensor representation, MBTR). Unfortunately, the size of both of these descriptors varies with the number of atomic species in a material, while established ML applications require a fixed descriptor size. Here we develop a method of encoding pairwise distance information called the Grouped Representation of Interatomic Distances (GRID). GRID encodes more information than RDF, whilst retaining its computational speed and insensitivity to the number of atoms present. GRID has been developed independently from a related approach (average minimum distance) which has recently been extended to pointwise distance distributions, and both have been proved to uniquely describe periodic point sets. To demonstrate the applicability of GRID to materials problems, we present its use in quantifying similarity between crystal structures as well as its effectiveness in predicting bulk modulus based on crystallographic structure.