| (*Contributed Equally) Inverse design of porous materials using artificial neural networks B Kim, S Lee, J Kim Science advances 6 (1), eaax9324, 2020 | 263 | 2020 |
| Applications of machine learning in metal-organic frameworks S Chong, S Lee, B Kim, J Kim Coordination Chemistry Reviews 423, 213487, 2020 | 127 | 2020 |
| *Computational screening of trillions of metal–organic frameworks for high-performance methane storage S Lee, B Kim, H Cho, H Lee, SY Lee, ES Cho, J Kim ACS Applied Materials & Interfaces 13 (20), 23647-23654, 2021 | 104 | 2021 |
| *User-friendly graphical user interface software for ideal adsorbed solution theory calculations S Lee, JH Lee, J Kim Korean Journal of Chemical Engineering 35, 214-221, 2018 | 92 | 2018 |
| *Predicting performance limits of methane gas storage in zeolites with an artificial neural network S Lee, B Kim, J Kim Journal of Materials Chemistry A 7 (6), 2709-2716, 2019 | 44 | 2019 |
| Finding hidden signals in chemical sensors using deep learning SY Cho, Y Lee, S Lee, H Kang, J Kim, J Choi, J Ryu, H Joo, HT Jung, ... Analytical chemistry 92 (9), 6529-6537, 2020 | 42 | 2020 |
| Size-Matching Ligand Insertion in MOF-74 for Enhanced CO2 Capture under Humid Conditions BL Suh, S Lee, J Kim The Journal of Physical Chemistry C 121 (44), 24444-24451, 2017 | 34 | 2017 |
| Finely tuned inverse design of metal–organic frameworks with user-desired Xe/Kr selectivity Y Lim, J Park, S Lee, J Kim Journal of Materials Chemistry A 9 (37), 21175-21183, 2021 | 24 | 2021 |
| Computational design of metal–organic frameworks with unprecedented high hydrogen working capacity and high synthesizability J Park, Y Lim, S Lee, J Kim Chemistry of Materials 35 (1), 9-16, 2022 | 15 | 2022 |
| New model for S-shaped isotherm data and its application to process modeling using IAST S Ga, S Lee, G Park, J Kim, M Realff, JH Lee Chemical Engineering Journal 420, 127580, 2021 | 14 | 2021 |
| Isotherm parameter library and evaluation software for CO2 capture adsorbents S Ga, S Lee, J Kim, JH Lee Computers & Chemical Engineering 143, 107105, 2020 | 10 | 2020 |
| (*Contributed Equally) Computational Analysis of Linker Defective Metal–Organic Frameworks for Membrane Separation Applications H Kim, S Lee, J Kim Langmuir 35 (11), 3917-3924, 2019 | 10 | 2019 |
| (*Contributed Equally) Machine learning-based discovery of molecules, crystals, and composites: A perspective review S Lee, H Byun, M Cheon, J Kim, JH Lee Korean Journal of Chemical Engineering, 1-12, 2021 | 8 | 2021 |
| (*Contributed Equally) Performance Evaluation of Deep Learning Architectures for Load and Temperature Forecasting under Dataset Size Constraints and Seasonality W Choi, S Lee Energy and Buildings, 113027, 2023 | 7* | 2023 |
| Deep learning-based initial guess for minimum energy path calculations H Park, S Lee, J Kim Korean Journal of Chemical Engineering 38, 406-410, 2021 | 1 | 2021 |
| *Chemical potential and solid-solid equilibrium of near-spherical Lennard-Jones dumbbell crystal S Lee, M Kim, J Chang Korean Journal of Chemical Engineering 33, 1047-1058, 2016 | 1 | 2016 |
| Real-time probabilistic backfill thermal property estimation method enabling estimation convergence judgment W Choi, S Lee, BH Dinh, YS Kim Case Studies in Thermal Engineering 48, 103108, 2023 | | 2023 |
| (*Contributed Equally) Interpretable deep learning model for load and temperature forecasting: Depending on encoding length, models may be cheating on wrong answers W Choi, S Lee Energy and Buildings, 113410, 2023 | | 2023 |
| Automatic Object Extraction from Electronic Documents Using Deep Neural Network H Jang, Y Chae, S Lee, J Jo KIPS Transactions on Software and Data Engineering 7 (11), 411-418, 2018 | | 2018 |
