계산촉매에너지연구실

■ We design various materials with the help of AI [ANN (Artificial Neural Network), LLM (Large Language Model), and MLIP (Machine Learning Interatomic Potential] and Data mining. ■ From AI-driven materials discovery, we (1) accelerate the simulation time for *DFT, (2) extend the simulation time and length scale with comparable accuracy with DFT, (3) (1) closed loop-based fully automated material design using data from experiments and DFT, and AI [Reinforce Learning]. * Density functional theory (DFT) is a standard tool in quantum chemistry and solid-state physics. ■ Among the environment-friendly and renewable energy technologies, solid oxide and proton exchange membrane fuel cells are promising candidates for future energy sources to produce electricity. ■ From computational approaches, we will design and develop new electrode and electrolyte materials for SOFCs and PEMFCs for our green energy sources in our future. ■ In addition to fuel cells, we also have interests in Li-ion batteries, S-ion batteries, functionalized materials for ionic conductor, hydrogen or CO2 storage, and utilization. ■ The research purpose of this topic is the development of exhaust gas purification system for the Super Ultra Low Energy & Emission Vehicle (SULEEV). ■ Due to the modification of engine driving conditions to achieve high fuel efficiency and low emission, the performance of existing purification system is declined. Thus, catalysts and adsorbents will be upgraded to exhibit higher performance at low temperature near 150°C. ■ In addition, the modeling technic for innovative engine-purification system will be developed and applied to bring out optimum engine condition and purification performance. ■ Finally, developed technologies will be commercialized by the technical transfer or est