Next generation batteries, such as those based off lithium-metal, are one of the most promising candidates for replacing current lithium-ion technology. While existing lithium-ion cells show adequate performance in many devices, there is an ever-expanding number of new technologies that call for higher performing secondary batteries. Unfortunately, the use of metallic lithium in batteries poses several safety issues, particularly when used in conjunction with conventional organic electrolytes1. A potential pathway to commercialisation of lithium-metal batteries (LMB’s) lies with the choice of electrolyte, and specifically the incorporation of ionic liquids2-3. However, due to the large number of cation-anion pairs possible, it can be quite time consuming and prohibitively expensive to experimentally screen all potential combinations. By gaining an understanding of the interaction occurring between the lithium and the ionic liquid cations and anions on the atomic scale, the electrolyte structure can be appropriately modified to tune the physical properties and obtain the desired behaviour and performance in a device. Incorporation of sulfonyl-based anions such as TFSI- and FSI- have shown promising properties for lithium-based cells4-5. Unfortunately, such materials have proven to be quite costly compared to traditional organic solvents, therefore, new anions need to be considered. Computational chemistry can assist with the selection of anions for LMB’s by screening many potential candidates6 in order to find potential electrolytes with the desired physical features and properties. In this study, several structurally modified anions based on sulfonyl anions are investigated using ab initio calculations to provide an understanding of how they contribute to battery performance, as well as to establish a link between anion structure and chemical behaviour (see Figure 1). Charge distribution, interaction energy and potential decomposition products are also explored to gain an understanding of their chemical behaviour within a device. From this data, trends are established between different modified anion structures and their associated properties.