Digital Chemistry for Sustainable Energy
Our research aims to understand the physicochemical properties of soft matter and the interfaces between soft and hard matters at the molecular level, with an emphasis on energy harvesting and storage applications. We are particularly interested in developing and applying computational methods and theoretical tools that play diverse roles in the discovery of novel energy materials, ranging from providing fundamental insights into molecular processes to predicting materials performance across broad chemical spaces.
Electrolytes: Towards conquering "the uncharted"
Towards molecular control of electrochemical interfaces
Towards AI/ML-assisted performance optimization
Computation plays a variety of roles in facilitating the advancement of energy materials, from providing fundamentals of their physicochemical properties to predicting their performance. However, there exists a fundamental trade-off in computation in chemical research between accuracy and speed, which sometimes limits its utility in spanning a wide range of chemical spaces. Now is a perfect time to develop new methods of molecular materials design that enable us to explore vast chemical spaces at unprecedented efficiency thanks to recent developments in AI and machine learning techniques. Currently, we are working on computation-aided battery health management. We are looking forward to sharing our fruits soon!