On the microscale, atomistic thermodynamic modeling can provide energy information and often is utilized to gain information missing in CALPHAD . The influence of atomic disorder on thermodynamic and mechanical properties has already been explored computationally for various families of crystalline materials including solid solution alloys. Given N lattice sites and p possible pure element types at each site, the dimensionality of the material space characterizing solid solution alloys scales like p N , which explodes for increasing values of N and/or p. A thorough and time-efficient exploration of such a highdimensional space requires fast (but still accurate) evaluations of the target property for every possible atomic arrangement. While state-of-the-art ab initio simulations, e.g. based on density functional theory (DFT), can yield highly accurate energetics and mechanical properties, their computational requirements still prohibit a thorough sampling of vast ranges of chemical composition and atomic configurations, even considering the computational power of massively parallel leadership supercomputers. Also, atomistic modeling of metal alloys is challenging because for non-stoichiometric compositions they are likely to form a disordered solid solution, and one needs to properly account for configurational entropy. Large-scale computational databases such as the Materials Project , the Open Quantum Materials Database (OQMD) , and the AFLOWlib database do not cover structures for sufficiently broad ranges of stoichiometry to cover relevant atomic configurations occuring in solid solution alloys.