Toward a Materials Genome Approach for Ionic Liquids: Synthesis Guided by <i>Ab Initio</i> Property Maps

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 Henry’s Law coefficient. Moreover, the property (CO₂, CH₄, and N₂ solubility) trends associated with exchange of the bis­(trifluoromethanesulfonyl)­amide anion with one of 32 other anions were explored and quantified