Sangwon Lee

Korea Advanced Institute of Science and Technology — South Korea

Chemistry · Physical Sciences

13h-index1.1kcitations27works1.52yr avg
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Research Topics

Metal-Organic Frameworks: Synthesis and Applications(11), Machine Learning in Materials Science(8), Zeolite Catalysis and Synthesis(6), Catalytic Processes in Materials Science(4), Covalent Organic Framework Applications(3)

Publications27 total

In Silico Generation of Chromium-Based MOFs with Abundant Active Sites for N<sub>2</sub>/CH<sub>4</sub> Separation
The Journal of Physical Chemistry C·2024· 3 cited
Explainable Kinetic Modeling for Aerobic Biodegradation of Poly(Lactic Acid) Using Bayesian Inferences
SSRN Electronic Journal·2024Open Access
Interpretable deep learning model for load and temperature forecasting: Depending on encoding length, models may be cheating on wrong answers
Energy and Buildings·2023· 2 cited
Computational Design of Metal–Organic Frameworks with Unprecedented High Hydrogen Working Capacity and High Synthesizability
Chemistry of Materials·2022· 44 cited
Machine learning-based discovery of molecules, crystals, and composites: A perspective review
Korean Journal of Chemical Engineering·2021· 16 cited
Computational Screening of Trillions of Metal–Organic Frameworks for High-Performance Methane Storage
ACS Applied Materials & Interfaces·2021· 206 cited
Anodic properties of Ni-Fe bimetallic nanofiber for solid oxide fuel cell using LaGaO3 electrolyte
Journal of Alloys and Compounds·2021· 13 cited
Deep learning-based initial guess for minimum energy path calculations
Korean Journal of Chemical Engineering·2021· 2 cited
Finely tuned inverse design of metal–organic frameworks with user-desired Xe/Kr selectivity
Journal of Materials Chemistry A·2021· 40 cited
Isotherm parameter library and evaluation software for CO2 capture adsorbents
Computers & Chemical Engineering·2020· 14 cited
Applications of machine learning in metal-organic frameworks
Coordination Chemistry Reviews·2020· 219 cited
Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning
2020· 3 citedOpen Access
Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning
2020· 1 citedOpen Access
Finding Hidden Signals in Chemical Sensors Using Deep Learning
Analytical Chemistry·2020· 73 cited
Inverse design of porous materials using artificial neural networks
Science Advances·2020· 317 citedOpen Access
Inverse Design in Porous Materials Using Artificial Neural Networks
2019· 30 citedOpen Access
Inverse Design in Porous Materials Using Artificial Neural Networks
2019· 16 citedOpen Access
Computational Analysis of Linker Defective Metal–Organic Frameworks for Membrane Separation Applications
Langmuir·2019· 11 cited
Predicting performance limits of methane gas storage in zeolites with an artificial neural network
Journal of Materials Chemistry A·2019· 42 cited
Size-Matching Ligand Insertion in MOF-74 for Enhanced CO<sub>2</sub> Capture under Humid Conditions
The Journal of Physical Chemistry C·2017· 48 cited
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Frequent Co-authors

Jihan Kim(17), Baekjun Kim(8), Junkil Park(2), Yunsung Lim(2), Jay H. Lee(2), Choi Youngik(2), Nak Chang Sung(2), Sanggyu Chong(1), Hyun Cho(1), Hooseung Lee(1), Sarah Yunmi Lee(1), Eun Seon Cho(1), Soo‐Yeon Cho(1), Youhan Lee(1), Hohyung Kang(1), Jaehoon Kim(1), Jung‐Hoon Choi(1), Jin Hwa Ryu(1), Heeeun Joo(1), Hee‐Tae Jung(1)