In this work we focus on models for a solid solution binary alloy, where two constituent elements are randomly placed on an underlying crystal lattice with pre-defined unit cell size. Nickel-niobium is one example where niobium and other metal elements are mixed in varying compositions to produce special nickel (Ni) steels and nickel-based super alloys. Niobium (Nb) enhances the mechanical properties, creep resistance and weldability of steels and super alloys . We therefore created and used a dataset for Ni-Nb alloys based on the EAM potential and made it available through the OLCF Constellation . The dataset includes both the formation energy as well as the bulk modulus for each alloy composition and structure. Each atomic sample has a disordered phase which is obtained starting from an initial regular crystal structure of type body-centered cubic (BCC), face-centered cubic (FCC), or hexagonal compact packed (HCP). Geometry optimization were performed using the LAMMPS simulation package to ensure that all the alloy samples reached an equilibrium geometry, characterized by negative formation energies relative to isolated atoms. The EAM potential is a reasonable choice for our purposes as it describes well the behaviors of the liquid and solid phases of Ni-Nb alloy . The structural factors and angular distributions of three atoms are well-matched with X-ray and ab initio-based molecular dynamics data. We prepared the three different crystals with different initial lattice parameters (3.52 Å for FCC, 3.32 Å for BCC, and 3.5 Å for HCP). We performed energy minimization in two steps. Firstly, we minimized the structures with an isotropic unit cell to minimize the side effects from our arbitrary lattice parameters for all other compositions. Then, we applied geometry optimization with a triclinic (non-orthogonal) unit cell to fully minimize the stress components to calculate the elastic constants. In this procedure, we chose 10,000 as the maximum number of allowable steps aimed at obtaining fully relaxed atomic geometries. Figure shows examples of relaxed geometries of initially BCC (a), FCC (b), and HCP (c) with different compositions. The input to the GCNN model for each sample includes the three components of the atom position and the atomic number. The predicted values are the formation energy and the bulk modulus. The dataset consists of three sets of crystal structures. The first set contains 46, 086 irregular crystal structures, each of them with 54 atoms, obtained through topology optimization starting from a regular BCC crystal structure. The second set contains 24, 543 irregular crystal structures, each of them with 32 atoms, obtained through topology optimization starting from a regular FCC crystal structure. The third set contains 39, 303 irregular crystal structures, each of them with 48 atoms, obtained through topology optimization starting from a regular HCP crystal structure. The atomic configurations within each set span the possible compositional range. The three sets have been merged into a global dataset, which is extremely heterogeneous in terms of crystal structures, lattice volumes, and atomic configurations. In order to ensure each composition is adequately represented in all portions of the dataset, splitting between the training, validation, and test sets was done separately for each composition.