Research
Theoretical and Computational Chemistry for a Sustainable Future
Research in the Kim group aims to understand the physicochemical properties of soft matter and the interfaces between soft and hard matters at the molecular level for energy harvesting and storage applications. We are particularly interested in utilizing computation that plays a variety of roles in facilitating the discovery of novel energy materials, from providing a fundamental understanding of molecular processes to predicting their performance spanning a wide range of chemical space.
Electrolytes: Towards conquering "the uncharted"
Developing fast solid ion conductors is a primary challenge in all solid batteries. Designing solid electrolytes requires considering several physicochemical properties, from short-range ion solvation to long-range ion transport. The Kim group is interested in providing the relationships between molecular properties across a wide range of time/length scales, such as ion solvation and ion transport, in solid electrolytes in terms of the formation of nanoscale ion networks. We aim to provide molecular principles for the innovative design of solid polymer electrolytes through systematic investigation using computation by broadening their design space with a wide range of salt concentrations.
Towards molecular control of electrochemical interfaces
Stabilizing the electrochemical interface is a great challenge in energy storage technology for large-scale energy grid systems, electric vehicles, and even small electronics. The Kim group is interested in understanding molecular processes that involve coupled charge/mass transfer and transport at electrochemical interfaces. We are developing a theory-/computation-driven "bridge" between molecular properties at the microscale and the energy device performance at the macroscale. Ultimately, we aim to provide molecular control of the electrochemical interfaces for enhanced safety and better performance.
Towards AI/ML-assisted molecular design
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 woking on computation-aided battery health management. We are looking forward to sharing our fruits soon!