Identifying promising anions for superionic single-ion conducting polymer electrolytes using data-science approaches

Polymer electrolytes are one of the most promising materials for next-generation energy systems due to their high stability, flexibility, and processibility. A major challenge is the development of highly conductive polymers with high cation transport number since polymeric electrolyte have yet to achieve the necessary properties for large-scale applications. We seek to use data-science approaches to provide insights into ion transport and expedite the design of superionic single ion conductors. Using ionic conductivity data collected from the literature and our own data, we test different methods of extracting the energy barrier for ion transport. The traditional Arrhenius fit to the temperature dependent ionic conductivity data indicates that the Meyer-Neldel rule holds true for single-ion conductors, but the values of fitting parameters do not provide physical explanation. Our modified method using fixed preexponent factor suggests that energy barriers should be temperature dependent, which may be characteristic for a broad range of temperature. With this in mind, we further use data-driven approaches and density functional theory (DFT) based calculations to connect the chemical structures of anions, binding energy of cation-anion pairs, and ionic conductivity. These approaches will assist in identifying promising chemical structures that lead to enhanced conductivity and guide the design of novel superionic single ion conducting polymer electrolytes.