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Jihan Kim

Korea Advanced Institute of Science and Technology · 化学

研究室紹介

Professor Jihan Kim's research lab specializes in computational materials science and artificial intelligence-driven materials discovery, focusing on the design and optimization of advanced porous materials for energy and environmental applications. The lab develops machine learning and deep generative models—such as generative adversarial networks and large language models—to predict, screen, and create novel metal-organic frameworks (MOFs) and zeolites with tailored properties for gas adsorption, separation, and sensing. Their work integrates large-scale molecular simulations, evolutionary algorithms, and high-throughput screening to identify materials with superior performance in methane storage, CO₂ capture, and selective gas sensing. The lab also pioneers AI systems like ChatMOF that bridge natural language understanding with materials informatics for intuitive and accurate materials exploration.

materials discoverymetal-organic frameworkszeolitesartificial intelligencegas separation

Research Overview

Papers
301
Total Citations
15,237
Papers (5y)
136
Primary Field
化学

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
136total
2022
2023
2024
2025
2026
Citations per year (5y)
2,299total
20222023202420252026

Selected Papers

15
1
Article|425 citations·2016
Superior Chemical Sensing Performance of Black Phosphorus: Comparison with MoS2 and Graphene
Soo‐Yeon Cho, Youhan Lee, Hyeong‐Jun Koh, Hyun-Ju Jung, Hyun-Ju Jung, Jong‐Seon Kim, Jong‐Seon Kim, Hae‐Wook Yoo, Jihan Kim, Jihan Kim, Hee‐Tae Jung, Hee‐Tae Jung
SJR Q1Advanced Materials

Superior chemical sensing performance of black phosphorus (BP) is demonstrated by comparison with MoS2 and graphene. Dynamic sensing measurements of multichannel detection show that BP displays highly sensitive, selective, and fast-responsive NO2 sensing performance compared to the other representative 2D sensing materials. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for on

Materials ChemistryMaterials Science
2
Article|324 citations·2020
Inverse design of porous materials using artificial neural networks
Baekjun Kim, Sangwon Lee, Jihan Kim
SJR Q1Science AdvancesOA

Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. Here, we have implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 121 crystalline porous materials. Our neural network ta

Materials ChemistryMaterials Science
3
Article|222 citations·2020
Applications of machine learning in metal-organic frameworks
Sanggyu Chong, Sangwon Lee, Baekjun Kim, Jihan Kim
SJR Q1Coordination Chemistry Reviews
Inorganic ChemistryChemistry
4
Article|209 citations·2021
Computational Screening of Trillions of Metal–Organic Frameworks for High-Performance Methane Storage
Sangwon Lee, Baekjun Kim, Hyun Cho, Hooseung Lee, Sarah Yunmi Lee, Eun Seon Cho, Jihan Kim
SJR Q1ACS Applied Materials & Interfaces

In the past decade, there has been an increasing number of computational screening works to facilitate finding optimal materials for a variety of different applications. Unfortunately, most of these screening studies are limited to their initial set of materials and result in a brute-force type of screening approach. In this work, we present a systematic strategy that can find metal-organic frameworks (MOFs) with the desired properties from an extremely diverse and large set of over 100 trillion

Inorganic ChemistryChemistry
5
Article|194 citations·2024
ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models
Yeonghun Kang, Jihan Kim
SJR Q1Nature CommunicationsOA

ChatMOF is an artificial intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4, GPT-3.5-turbo, and GPT-3.5-turbo-16k), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid and formal structured queries. The system is comprised of three core components (i.e., an agent, a toolkit, and an evaluator) and it forms a robust pipeline that m

Materials ChemistryMaterials Science
6
Article|168 citations·2023
A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks
Yeonghun Kang, Hyunsoo Park, Berend Smit, Jihan Kim
SJR Q1Nature Machine IntelligenceOA
Inorganic ChemistryChemistry
7
Article|162 citations·2013
New materials for methane capture from dilute and medium-concentration sources
Jihan Kim, Amitesh Maiti, Li‐Chiang Lin, Joshuah K. Stolaroff, Berend Smit, Roger D. Aines
SJR Q1Nature CommunicationsOA
Inorganic ChemistryChemistry
8
Article|152 citations·2017
User-friendly graphical user interface software for ideal adsorbed solution theory calculations
Sangwon Lee, Jay H. Lee, Jihan Kim
SJR Q2Korean Journal of Chemical Engineering
Mechanical EngineeringEngineering
9
Article|140 citations·2012
Predicting Large CO2 Adsorption in Aluminosilicate Zeolites for Postcombustion Carbon Dioxide Capture
Jihan Kim, Li‐Chiang Lin, Joseph A. Swisher, Maciej Harańczyk, Berend Smit
SJR Q1Journal of the American Chemical SocietyOA

Large-scale simulations of aluminosilicate zeolites were conducted to identify structures that possess large CO(2) uptake for postcombustion carbon dioxide capture. In this study, we discovered that the aluminosilicate zeolite structures with the highest CO(2) uptake values have an idealized silica lattice with a large free volume and a framework topology that maximizes the regions with nearest-neighbor framework atom distances from 3 to 4.5 Å. These predictors extend well to different Si:Al rat

Inorganic ChemistryChemistry
10
Article|121 citations·2013
Large-Scale Screening of Zeolite Structures for CO2 Membrane Separations
Jihan Kim, Mahmoud Kamal Forrest Abouelnasr, Li‐Chiang Lin, Berend Smit
SJR Q1Journal of the American Chemical SocietyOA

We have conducted large-scale screening of zeolite materials for CO2/CH4 and CO2/N2 membrane separation applications using the free energy landscape of the guest molecules inside these porous materials. We show how advanced molecular simulations can be integrated with the design of a simple separation process to arrive at a metric to rank performance of over 87,000 different zeolite structures, including the known IZA zeolite structures. Our novel, efficient algorithm using graphics processing u

Inorganic ChemistryChemistry
11
Article|120 citations·2023
Three-Dimensional MoS2/MXene Heterostructure Aerogel for Chemical Gas Sensors with Superior Sensitivity and Stability
Seulgi Kim, Hamin Shin, Jaewoong Lee, Chungseong Park, Yunhee Ahn, Hee‐Jin Cho, Seoyeon Yuk, Jihan Kim, Dongju Lee, Il‐Doo Kim
SJR Q1ACS Nano

The concept of integrating diverse functional 2D materials into a heterostructure provides platforms for exploring physics that cannot be accessed in a single 2D material. Here, physically mixing two 2D materials, MXene and MoS 2, followed by freeze-drying is utilized to successfully fabricate a 3D MoS 2 /MXene van der Waals heterostructure aerogel. The low-temperature synthetic approach effectively suppresses significant oxidation of the Ti 3 C 2 T x MXene and results in a hierarchical and free

Materials ChemistryMaterials Science
12
Article|118 citations·2019
Computer-aided discovery of connected metal-organic frameworks
Ohmin Kwon, Jin Yeong Kim, Sungbin Park, Jae Hwa Lee, Junsu Ha, Hyunsoo Park, Hoi Ri Moon, Jihan Kim, Jihan Kim, Jihan Kim
SJR Q1Nature CommunicationsOA

Composite metal-organic frameworks (MOFs) tend to possess complex interfaces that prevent facile and rational design. Here we present a joint computational/experimental workflow that screens thousands of MOFs and identifies the optimal MOF pairs that can seamlessly connect to one another by taking advantage of the fact that the metal nodes of one MOF can form coordination bonds with the linkers of the second MOF. Six MOF pairs (HKUST-1@MOF-5, HKUST-1@IRMOF-18, UiO-67@HKUST-1, PCN-68@MOF-5, UiO-6

Inorganic ChemistryChemistry
13
Article|107 citations·2012
Large-Scale Computational Screening of Zeolites for Ethane/Ethene Separation
Jihan Kim, Li‐Chiang Lin, Richard L. Martin, Joseph A. Swisher, Maciej Harańczyk, Berend Smit
SJR Q1LangmuirOA

Large-scale computational screening of thirty thousand zeolite structures was conducted to find optimal structures for separation of ethane/ethene mixtures. Efficient grand canonical Monte Carlo (GCMC) simulations were performed with graphics processing units (GPUs) to obtain pure component adsorption isotherms for both ethane and ethene. We have utilized the ideal adsorbed solution theory (IAST) to obtain the mixture isotherms, which were used to evaluate the performance of each zeolite structu

Inorganic ChemistryChemistry
14
Article|103 citations·2017
Excavating hidden adsorption sites in metal-organic frameworks using rational defect engineering
Sanggyu Chong, Günther Thiele, Jihan Kim
SJR Q1Nature CommunicationsOA

Metal-organic frameworks are known to contain defects within their crystalline structures. Successful engineering of these defects can lead to modifications in material properties that can potentially improve the performance of many existing frameworks. Herein, we report the high-throughput computational screening of a large experimental metal-organic framework database to identify 13 frameworks that show significantly improved methane storage capacities with linker vacancy defects. The candidat

Inorganic ChemistryChemistry
15
Article|96 citations·2022
Oxide/ZIF‐8 Hybrid Nanofiber Yarns: Heightened Surface Activity for Exceptional Chemiresistive Sensing
Dong‐Ha Kim, Sanggyu Chong, Chungseong Park, Jaewan Ahn, Ji‐Soo Jang, Jihan Kim, Il‐Doo Kim
SJR Q1Advanced Materials

Abstract Though highly promising as powerful gas sensors, oxide semiconductor chemiresistors have low surface reactivity, which limits their selectivity, sensitivity, and reaction kinetics, particularly at room temperature (RT) operation. It is proposed that a hybrid design involving the nanostructuring of oxides and passivation with selective gas filtration layers can potentially overcome the issues with surface activity. Herein, unique bi‐stacked heterogeneous layers are introduced; that is, n

Electrical and Electronic EngineeringEngineering

Research Areas

Inorganic ChemistryMaterials ChemistryElectrical and Electronic EngineeringMechanical EngineeringRenewable Energy, Sustainability and the EnvironmentControl and Systems Engineering

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