The Materials Genome Approach (MGA) aims to accelerate development of new materials by incorporating computational and data-driven approaches to reduce the cost of identification of optimal structures for a given application. Here, we use the MGA to guide the synthesis of triazolium-based ionic liquids (ILs). Our approach involves an IL property-mapping tool, which merges combinatorial structure enumeration, descriptor-based structure representation and sampling, and property prediction using molecular simulations. The simulated properties such as density, diffusivity, and gas solubility obtained for a selected set of representative ILs were used to build neural network models and map properties for all enumerated species. Herein, a family of ILs based on ca. 200 000 triazolium-based cations paired with the bis(trifluoromethanesulfonyl)amide anion was investigated using our MGA. Fourteen representative ILs spreading the entire range of predicted properties were subsequently synthesized and then characterized confirming the predicted density, diffusivity, and CO2 Henrys Law coefficient. Moreover, the property (CO2, CH4, and N-2 solubility) trends associated with exchange of the bis(trifluoromethanesulfonyl)amide anion with one of 32 other anions were explored and quantified.