Design through computation, theory, and materials informatics.
The crosscut goal is to leverage fundamental knowledge obtained from the Thrusts to enable the design of fast ion conducting polymer and polymer-ceramic electrolytes.
Key questions
- How can representation learning be utilized to combine experimental and simulation data to discover high-performing polymer electrolytes with superior charge transport?
- How can theoretical and computational approaches with vastly different data types be combined in an interconnected, seamless, multiscale manner to predict bulk and interfacial properties critical for fast, cooperative ion-conducting polymer-based electrolytes?