YONGCHUL CHUNG

Associate Professor

Pusan National University

South Korea

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About

Yongchul Chung is an Associate Professor at Pusan National University, South Korea. His research focuses on techno-economic analysis and computational modeling of materials for gas separation and storage applications, with recent articles exploring metal-organic frameworks and their efficiency in various processes. He has also contributed to advancements in machine learning approaches for material classification and optimization in energy applications.

Articles (23)

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2025

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2022

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2022

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2022

Collaborators (7)

ZHAO GUOBIN

Teaching Assignment

Pusan National University

SOUTH KOREA

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Dae-Won Park

Pusan National University

SOUTH KOREA

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Aditya Nandy

Assistant Professor of Chemical and Biomolecular Engineering

University of California, Los Angeles

UNITED STATES

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Inkyu Lee

Professor

Pusan National University

SOUTH KOREA

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JINO C JOHN

Research Professor

Yeungnam University

SOUTH KOREA

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Poulumi Dey

NETHERLANDS

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Peyman Z. Moghadam

Associate Professor

University College London

UNITED KINGDOM

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YONGCHUL CHUNG

Research Interests

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About

Articles (23)

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal–Organic Frameworks

Techno-economic analysis of Cu(I)-loaded porous carbons for CO separation via pressure/vacuum swing adsorption process

Correction to “Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF6 Replacement Gases”

Modeling, screening, and techno-economic evaluation of metal–organic frameworks for boil-off gas capture during intercontinental transportation of LNG

Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications

Transfer learning-assisted computational screening of metal-organic frameworks and covalent-organic frameworks for the separation of Xe/Kr noble gas

Multi-scale computational screening of all-silica zeolites for adsorptive separation of ternary (H2S/CO2/CH4) mixtures

Correction to “PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks”

Anion storing, oxygen vacancy incorporated perovskite oxide composites for high-performance aqueous dual ion hybrid supercapacitors

PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks

High Hydrogen Storage in Trigonal Prismatic Monomer‐Based Highly Porous Aromatic Frameworks

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF6 Replacement Gases

Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic Frameworks (COFs): Force-Field and Machine Learning Models

A database of ultrastable MOFs reassembled from stable fragments with machine learning models

High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters

Polyoxometalate–polymer hybrid artificial layers for ultrastable and reversible Zn metal anodes

SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms

Effective delamination of a layered two-dimensional MCM-22 zeolite: Quantitative insights into the role of the delaminated structure on acid catalytic reactions

Defect-engineered MOF-801 for cycloaddition of CO2 with epoxides

Brunauer–Emmett–Teller Areas from Nitrogen and Argon Isotherms Are Similar

Microplasma Band Structure Engineering in Graphene Quantum Dots for Sensitive and Wide-Range pH Sensing

Defect-engineered MOF-801 for cycloaddition of CO<sub>2</sub> with epoxides

Discovery of High-Performing Metal–Organic Frameworks for On-Board Methane Storage and Delivery via LNG–ANG Coupling: High-Throughput Screening, Machine Learning, and Experimental Validation

Collaborators (7)

ZHAO GUOBIN

Dae-Won Park

Aditya Nandy

Inkyu Lee

JINO C JOHN

Poulumi Dey

Peyman Z. Moghadam

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