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.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Superior 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
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
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
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
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
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
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
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
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
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
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