Predicting the Ionic Conductivity and Obtaining Mechanistic Insights of Plasticized Solid Polymer Electrolytes Using a Data-Driven Approach

Solid polymer electrolytes (SPEs) are critical for the development of safe and high-performance solid-state lithium batteries powering the next generation of electric vehicles, drones, and robotics. To date, one of the major disadvantages of SPEs limiting their wide applications is the low ionic conductivity. The use of ionic liquid as plasticizers in SPEs has been demonstrated as a promising strategy to enhance the ionic conductivity of SPEs at room temperature while maintaining their safety features and mechanical properties. However, the optimization of plasticizers is largely intuition-based, without general design rules and predictive design strategies. Therefore, in this work, we developed a fast and low-cost data-driven workflow that advances the design and optimization of ionic liquid plasticizers for SPEs. Using this approach, we successfully identified a new plasticized SPE material that showed high ionic conductivity and superior cycling stability. Our data-driven model revealed important design factors correlated to highly effective plasticizers, providing insights into the conduction mechanism of plasticized SPEs. More importantly, we found that these key factors are transferrable and applicable in other plasticized SPEs beyond our dataset, highlighting the generality of the findings obtained by our data-driven approach.