Research
ARTIFICIAL INTELLIGENCE for display and energy
The utilization of organic compounds spans various technological applications, from display devices (i.e., OLED screens and next-generation electrochemical devices) to energy devices (i.e., photovoltaic cells and secondary batteries), owing to their unique properties, such as flexibility, lightweight nature, and tunable electronic properties. Understanding the dielectric (i.e., dielectric strength and dielectric constant) and electrochemical (i.e., electron affinity and frontier orbitals) properties of organic compounds is therefore essential for strategic material design.
Artificial intelligence in tandem with the computational modeling approach can assist us to establish a robust protocol for accurately predicting the dielectric and electrochemical properties across a comprehensive array of organic compounds.
Rechargeable batteries
Computational exploration of organic/inorganic materials for electrodes in rechargeable batteries is essential for a successful development of high-performance electrodes.
This computational protocol can be further employed for the efficient utilization in the electrochemical fields, including water splitting systems!
hydrogen storage
Metal hydrides suffer from their poor characteristics in terms of reaction thermodynamics and kinetics despite their potential for on-board hydrogen storage systems.
Modulating metal hydride reaction thermodynamics combined with particle size control would be a promising approach to improve the poor characteristics of metal hydride reactions!
metal-organic frameworks
Metal-organic frameworks with tailored characteristics in their textural, electronic, and chemical properties are highly porous, crystalline compounds that can be widely applied to gas separation/storage, display, and catalysis.
High-throughput computational screening, machine learning, and other advanced computational techniques can be integrated to develop a computational solution that can suggest desired directions for designing metal-organic frameworks with high-performance!
NANO-catalysis
The catalytic performance relies not only on the identity of active sites but also on the local geometry.
Therefore, computational chemistry combined with molecular dynamics simulation approach would be a robust tool to comprehensively investigate the catalytic reactions on a variety of catalytic sites!
water splitting CaTALYsts
Computational chemistry is a great tool to design promising catalysts for water splitting applcations and predict various performance parameters.
Furthermore, a systematic studies on catalytic performance parameters for a broad array of catalyst surfaces would allow us to understand structure-performance relationship!