Data analytics helps quickly identify top candidates, such as polymers, ionic materials, alloys, ceramics, and composites, for a range of applications. The approach allows experimentalists to target a few potential candidates to synthesize compared to a broad set of materials, which assist organizations in better managing their resources.
Few examples .....
Better electrolyte, cathode, or anode materials for battery applications
Materials for structural applications
Materials for sustainable future
We employ multi-scale modeling to identify novel material with superior properties for various applications. We perform simulations from quantum to meter scale to determine critical parameters responsible for observed properties. Key parameters assist experimentalists in synthesizing materials with better efficiency than the traditional Edisonian approach.