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

Multi-scale modeling

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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.      

Data Analytics for selection and design of materials

We utilize data analytics methods, especially machine learning algorithms, to develop a relationship between material inherent properties at various length scales and material macro properties. Due to the vast number of materials data available from multiple resources, the data analytics techniques would be beneficial to find a relationship between various material features and the bulk properties. The outcome of the relationship would be helpful for material selection for a specific application or designing novel materials with preferred macro properties.