Autonomous Energy Materials Discovery

Autonomous Energy Materials Discovery [AMD] research group aims to apply fundamental physical laws and modern data science tools to model, and more ambitiously, to discover new functional materials for energy conversion and storage. To increase the speed and prediction capability of advanced materials properties, AMD is actively developing and using multi-scale high-throughput virtual screening frameworks that are powered by classical and quantum simulations, and data-driven methods.

 

AMD has three highly integrated research lines:

BAT: High-throughput computational methods for BATtery materials discovery

CAT: High-throughput computational methods for electroCATalytic materials discovery

DAT: DATa science methods for functional energy materials discovery

 

AMD consists of researchers from diverse disciplines of basic sciences and engineering. AMD is always interested in collaborating with research and industrial organizations that are essential for benchmarking and validation of the computer predicted results.