We focus on the development and application of data-driven methods and multi-scale modeling tools in the broad area of energy and sustainability.
Current Research Interests
Separation (carbon capture, Xe/Kr separation)
Energy (hydrogen, electrolytes)
Water (water harvesting, adsorption-based heat pump, ion separation)
Group News
2025
May
Guobin, Sunghyun, and Haewon's paper on CoRE MOF DB, in collaboration with 9 institutions worldwide (Northwestern, Minnesota, Georgia Tech, MIT, Berkeley, Toronto, CNRS, Oak Ridge, IMDEA), was published online in Matter
February
Sunghyun's paper on modeling and techno-economic analysis of boil-off gas treatment using MOF was published in Chemical Engineering Journal.
2024
November
Haewon won the Grand Prize at the first Medical AI competition! Congratulations!
October
Undergraduate students Taekgi and Jaehoon won Samsung AI Challenge! Congratulations!
Yu published a paper on developing force field parameters for Mg-catecholate and hydrogen and high-throughput screening of covalent-organic frameworks with Delft University of Technology and Huazhong University Science & Technology at ACS Applied Materials & Interface.
September
Sunghyun and Muhammad's paper on pressure-vacuum swing adsorption modeling and optimization for ternary system is published at Chemical Engineering Journal.
Haewon published a collaborative paper on modeling aqueous zinc metal batteries with SKKU at Advanced Functional Materials.
August
Muhammad successfully defended Master's degree in Chemical Engineering. He will be continuing in the group as a Ph.D. student. Congratulations!
June
Guobin's paper on machine learning model to predict partial atomic charges in porous materials is published at the Journal of Chemical Theory and Computation.
April
Yu published a collaborative paper on modeling porous aromatic framework with Korea University at Advanced Material.
Guobin and Haewon published a paper on machine learning model development for Low-GWP and lifetime of molecule at Journal of Physical Chemistry A.
February:
Prof. Chung published a collaborative review paper on computational MOF discovery at Nature Energy