Ionic liquids are a class of salts that are liquid at room temperature with several unique and desirable properties such as negligible vapor pressure, environmentally friendly, high thermal and electrochemical stability. Because of these favorable properties, ionic liquids are currently considered a potential replacement for conventional organic electrolytes in Li-ion batteries. However, a significant limitation of ionic liquids is their sluggish transport properties limiting their widespread application in battery technology. This drawback can be tackled by choosing the right ionic liquids based on chemical intuition that could improve transport properties. However, the vast chemical space of ionic liquids for such manipulation could be expensive and unfeasible using experimental and computational simulations. The increased capability of machine learning techniques combined with the availability of experimental data in the open literature for ionic liquids provides an excellent opportunity to develop algorithms to search and discover high-performance ionic liquids suitable for industrial applications rapidly at a low cost.
To accelerate the discovery of ionic liquids matching key criteria for electrolytes, OSU Researchers have developed a Machine Learning algorithm to search for ionic liquids with high performance. The developed Machine Learning model was able to identify ionic liquids with the highest conductivity (greater than 1 S/m) and able to predict the ionic conductivity for several binary and reciprocal ionic liquid mixtures. Compared to experimental data available in the open literature, the algorithm was able to discover solvents with a performance increase of 27% at room temperature. This accelerated discovery of high-performing ionic liquids could narrow down the search and reduce the overall experimental and computational cost for the industry.
Machine learning algorithm for accelerated discovery of ionic liquids
High accuracy & Rapid discovery
Narrows down chemical search space