HANS Lab

The High-throughput Ab-initio Nanomaterials Simulations (HANS) Lab, led by Professor Byungchan Han in the Department of Chemical and Biomolecular Engineering at Yonsei University, integrates quantum mechanics-based computational chemistry, multiscale materials simulation, and machine learning and physics-based AI technologies to elucidate the operation mechanisms and design principles of next-generation energy materials. Based on density functional theory (DFT), molecular dynamics (MD), thermodynamics, and reaction kinetics analyses, our lab unravels the “structure-property-performance” relationship of various materials at the atomic and electronic levels. By integrating these findings with machine learning, our lab is expanding its research scope to include complex catalytic surfaces, electrode/electrolyte interfaces, nanoparticles, and solid electrolyte systems – areas that were previously difficult to address using conventional computational methods. The strength of our laboratory lies in going beyond simple property prediction to elucidate how materials change and exhibit performance under “actual operating conditions”. Targeting a wide range of energy and environmental systems – including water electrolysis, metal-air batteries, all-solid-state batteries, seawater electrolysis, nanomaterial growth, and membrane materials – we are efficiently exploring vast material spaces using machine learning-based interatomic potentials, active learning, high-throughput screening, and data-driven catalyst design techniques. We are building a predictive material design platform that bridges computation and experiments. [한병찬 / 연세대 화공생명공학과 교수 : 사고 수습할 때 제일 중요한 것은 사고 수습하는 사람들한테 어떤 물질이 있고, 어떤 상태에 원자로가 놓여 있다는 것을 제공해 줘야 합니다. 그것 없이는 절대 접근하지 않을 거예요.]