Kaist

Professor Jihyeon Yeom Selected as Early Career Ad..

  < Professor Jihyeon Yeom >     KAIST announced on the 13th that Professor Jihyeon Yeom from the Department of Materials Science and Engineering has been selected as a member of the Early Career Advisory Board (ECAB) for Chemical Reviews, widely considered the world's most prestigious academic journal in the field of chemistry. Published by the American Chemical Society (ACS), Chemical Reviews is a flagship review journal that comprehensively organizes and surveys the most influential research achievements across all areas of chemistry and materials science. It is evaluated as a top-tier international journal in the field. The journal boasts an Impact Factor (IF) of 56, ranking it among the highest of all scientific journals worldwide. Its authority is particularly significant because it is a review journal that analyzes global research trends to suggest future academic directions, rather than simply publishing individual experimental data. The ECAB, which began its term in January 2026, consists of 10 researchers selected from among rising global science leaders. Candidates are evaluated based on academic originality, research impact, and contributions to the scientific community. Members provide advisory roles for the journal's academic direction and strategic planning, contributing to the discovery of next-generation research trends and the expansion of global research networks. This selection highlights that Professor Yeom’s research achievements are receiving high international acclaim. Professor Yeom is conducting research on applying "chirality"—a property where objects, like DNA or proteins, are mirror images of each other but cannot be perfectly superimposed—to nanomaterials. Her core work involves precisely controlling atomic arrangements to realize artificial materials that can interact naturally with biological signals. In particular, she is gaining attention for developing next-generation smart healthcare technology that combines light-responsive chiral materials with Artificial Intelligence (AI) to detect and analyze minute changes in the human body in real time. Professor Yeom explained that these chiral characteristics offer new possibilities for expanding information transmission and processing capabilities beyond simple structural properties. Building on this foundation, she plans to expand her research into various fields, including precision medical diagnostic technology, next-generation optoelectronic devices utilizing circularly polarized light, and AI-based platforms. Professor Yeom has established herself as a global leader in chiral materials research, recently publishing results in world-renowned journals such as Nature Communications, Advanced Materials, ACS Nano, and Accounts of Chemical Research. "Chirality is not just a structural characteristic, but a new degree of freedom that expands the functional and information-processing capabilities of matter," said Professor Yeom. "I plan to expand my research into chiral-based electronic and optical devices, bio-diagnostic technologies, and AI-based spectroscopic platforms in the future." This ECAB selection once again demonstrates the research competitiveness and international standing of the KAIST Department of Materials Science and Engineering. It is expected to further strengthen KAIST's role as a global research hub in the field of next-generation materials research.    

KAIST Solves the 500-Year-Old ‘Pain’ Behind Michel..

  <(From Left) Ph.D candidate Minwoo Choi, Ph.D candidate Hyejoon Jun, Professor Hyoungsoo Kim>      More than 500 years ago, Michelangelo spent four years painting The Creation of Adam on the ceiling of the Sistine Chapel, struggling with paint dripping onto his face. He described the process as “closer to torture than painting.” Now, researchers at KAIST have developed a technology that can effectively “hold up falling paint.” Beyond ceiling paintings, this principle could help solve the problem of liquid films collapsing on inclined surfaces, with potential applications in precision coating, electronic circuit printing, 3D printing, and fluid control in space environments. KAIST (President Kwang Hyung Lee) announced on the 12th of March that a research team led by Professor Hyungsoo Kim of the Department of Mechanical Engineering has reinterpreted the fundamental cause of downward flow under gravity—known as gravitational instability—from the perspective of interfacial fluid mechanics＊ and proposed a method to control it by mixing a small amount of volatile liquid into a suspended liquid film. ＊Interfacial fluid mechanics: the study of the balance of microscopic forces acting at the surface of liquids. Why was it so difficult for Michelangelo to paint on the ceiling? When paint is applied to a ceiling, a thin liquid film forms. However, this film gradually becomes unstable due to gravity and eventually drips down. This phenomenon is common in everyday life. For example, when steam condenses on a bathroom ceiling, it first forms a thin layer of water that eventually gathers into droplets and falls. Similarly, droplets that appear on the ceiling of a refrigerator initially form a thin layer but gradually grow and begin to drip downward. This type of instability, where liquid accumulated on an upper surface collapses under gravity, is known as Rayleigh–Taylor instability. Until now, it has generally been considered unavoidable in the presence of gravity. The research team proposed mixing a small amount of volatile liquid into the suspended liquid film. As the volatile component evaporates, it changes the concentration distribution along the liquid surface, creating differences in surface tension. Surface tension is the force that pulls a liquid surface inward, which is why water droplets maintain a rounded shape. When differences in surface tension arise, the region with stronger tension pulls liquid toward itself from regions with weaker tension. This creates a surface flow known as the Marangoni effect. Through experiments and theoretical analysis, the researchers demonstrated that this surface flow can effectively hold the liquid in place and suppress the gravitational instability that would otherwise cause it to fall. A familiar example can illustrate this effect. If pepper powder is sprinkled evenly on the surface of water, it remains floating. However, if a drop of detergent is placed in the center, the pepper suddenly moves outward toward the edges. This happens because the detergent reduces the surface tension where it touches the water, allowing the surrounding regions with stronger surface tension to pull the liquid outward. As the surface flow develops, the pepper particles move along with it. In this study, evaporation of the volatile liquid created a similar surface tension difference. But instead of pushing particles outward like in the pepper example, the flow pulled the liquid upward, counteracting the force that would otherwise cause it to drip downward. As a result, under certain conditions the liquid film remained intact despite gravity. In some cases, the researchers even observed a new behavior in which droplets did not fall but the liquid film oscillated periodically. This demonstrates that gravitational instability can be actively controlled using only natural processes—such as liquid composition and evaporation—without any external energy input. This principle could enable thinner and more uniform liquid films in precision coating, printing, and layer-by-layer manufacturing processes, allowing stable coating even on tilted surfaces. It may also extend to technologies such as 3D printing and fluid control in specialized environments like space. In essence, the physical limitation that Michelangelo struggled with 500 years ago may now inspire future industrial technologies. <A fictional staged scene of Michelangelo painting The Creation of Adam (AI-generated image)> Professor Hyungsoo Kim stated, “Rayleigh–Taylor instability has long been considered unavoidable as long as gravity exists. This research is meaningful because it shows that gravitational instability can be actively controlled without external energy by utilizing natural processes such as liquid composition and evaporation.” He added, “This principle could extend beyond coating, printing, and layering processes to fluid control technologies in space environments.” This study was led by Minwoo Choi, an integrated master’s–PhD student in Mechanical Engineering, as the first author. The discovery, recognized as a new finding in the control of hydrodynamic instability, was published online on January 29 in the international journal Advanced Science (Wiley) and was selected as a Frontispiece article. ※ Paper title: “Evaporation-Driven Solutal Marangoni Control of Rayleigh–Taylor Instability in Inverted Films,” Authors: First author Minwoo Choi (KAIST), co-author Hyejoon Jun (KAIST), corresponding author Hyungsoo Kim (KAIST),  DOI: https://doi.org/10.1002/advs.202520343 This research was supported by the Mid-Career Researcher Program of the National Research Foundation of Korea (MSIT: 2021R1A2C2007835)       

KAIST Develops mRNA Platform That Remains Effectiv..

  <(From Left) Dr. Subin Yoon, Ph.D candidate Hyeonggon Cho, Prof. Jae-Hwan Nam, Prof. Young-suk Lee>     Since the COVID-19 pandemic, mRNA vaccines have gained attention as a next-generation pharmaceutical technology. mRNA therapeutics work by delivering genetic instructions that enable cells to produce specific proteins for therapeutic effects. However, their efficacy has been reported to decline in elderly individuals or patients with obesity. To address this limitation, Korean researchers have newly designed a key regulatory region of mRNA that improves therapeutic protein production efficiency, developing a next-generation mRNA platform that maintains effectiveness even in aging and obesity conditions. KAIST (President Kwang Hyung Lee) announced on the 10th of March that a joint research team led by Professor Young-suk Lee of the Department of Bio and Brain Engineering and Professor Jae-Hwan Nam of The Catholic University of Korea (President Jun-Gyu Choi) has developed a new mRNA platform by precisely designing the sequence of the 5′ untranslated region (5′UTR)＊, a key regulatory region of mRNA. ＊5′ untranslated region (5′UTR): A region of mRNA that initiates and regulates protein production. The design of this region influences both the amount and speed of protein synthesis. The research team analyzed large-scale bioinformatics datasets to identify 5′UTR sequences that enable proteins to be produced more efficiently across diverse cellular environments. When applied, the designed sequences significantly enhanced protein production and immune responses even in preclinical models of aging and obesity. mRNA is a long single-stranded RNA molecule that serves as the blueprint for producing proteins required by the body. It consists of several components: the 5′UTR, which initiates and regulates the rate of protein production; the coding sequence (CDS), which contains the genetic information for a specific protein; the 3′ untranslated region (3′UTR), which helps maintain mRNA stability within cells; and the poly(A) tail, which further enhances stability and supports protein synthesis. Among these components, the 5′UTR and 3′UTR do not determine the type of protein produced, but they play a critical role in regulating how efficiently the protein is synthesized. For this reason, these regions are receiving increasing attention as key bioengineering platforms for improving the performance of various mRNA therapeutics, including vaccines and treatments. <Schematic Diagram of mRNA Therapeutic Design and Validation Using Bioinformatics> To identify highly efficient 5′UTR sequences capable of promoting protein production across multiple tissues and cellular environments, the team conducted an integrated analysis of large-scale biological datasets. This included multiple analytical approaches such as RNA sequencing (RNA-seq) for analyzing gene activity across tissues, single-cell RNA sequencing (scRNA-seq) for examining gene expression at the individual cell level, and ribosome profiling (Ribo-seq) for measuring actual protein translation efficiency. The researchers also focused on the fact that in aging or obesity conditions, cells often experience high levels of stress—particularly oxidative stress—which can reduce their ability to synthesize proteins. When the newly designed mRNA therapeutics were applied to preclinical models of aging and obesity, the results showed significantly improved protein production and immune responses compared with existing approaches. This research is expected to be applicable not only to mRNA vaccines but also to a wide range of biopharmaceutical technologies, including gene therapies and immunotherapies. <Multimodal Bio–Big Data Analysis–Based mRNA Therapeutic Design (AI-Generated Image)> Professor Young-suk Lee of KAIST Department of Bio and Brain Engineering stated, “This study identified a design strategy that enables mRNA to produce proteins more efficiently by analyzing large-scale biological data,” adding, “This technology will provide an important foundation for ensuring that mRNA vaccines and therapeutics remain effective even in environments where drug efficacy may decline, such as in elderly or obese patients.” In this study, Dr. Subin Yoon from The Catholic University of Korea and doctoral candidate Hyeonggon Cho from KAIST participated as co-first authors. The research findings were published online on January 2 in the internationally renowned journal Molecular Therapy (IF = 12.0), a leading journal in gene and cell therapy. (Paper title: ”Designing 5′UTR sequences improves the capacity of mRNA therapeutics in preclinical models of aging and obesity” DOI: https://doi.org/10.1016/j.ymthe.2025.12.060) This research was supported by the Excellent Young Researcher Program and the Bio-Medical Technology Development Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT, the Infectious Disease Response Innovative Technology Support Program of the Ministry of Food and Drug Safety, and the Infectious Disease Prevention and Therapeutics Technology Development Program of the Korea Health Industry Development Institute.  

KAIST Surpasses the Limits of AlphaFold… AI Now Pr..

  <(From Left) Ph.D candidate Hyojin Son, Professor Gwan-su Yi>     Proteins in our body function like switches. When a drug binds to a protein, the structure at the binding site changes, and this structural change propagates throughout the protein, turning its function on or off. Google DeepMind’s AlphaFold3 successfully predicted whether drugs bind to proteins and the three-dimensional structure of binding sites. However, it could not predict how signals propagate inside the protein after drug binding, how the entire structure changes, or whether the protein’s function is ultimately activated or inhibited. KAIST researchers have developed an AI that predicts not only whether a drug binds but whether it actually works. KAIST (President Kwang Hyung Lee) announced on the 4th of March that a research team led by Professor Gwan-Su Yi of the Department of Bio and Brain Engineering has developed an artificial intelligence model called “GPCRact” that predicts whether candidate molecules not only bind to G-protein-coupled receptors (GPCRs)—a major drug target—but also actually activate the protein. GPCRs act as “signal receivers” on the surface of cells. When hormones, neurotransmitters, or drugs send signals from outside the cell, GPCRs function as gates that receive these signals and transmit them into the cell. There are about 800 types of GPCRs in the human body, and roughly 30–40％ of currently marketed drugs target them. They are key proteins involved in numerous physiological functions, including heart rate regulation, blood pressure control, pain sensing, immune responses, and emotional regulation. However, a drug binding to a GPCR does not always trigger the desired biological function. Structural changes inside the protein and subsequent signal transmission determine whether the drug actually produces an effect. This process is known as allosteric signal propagation. The research team designed the AI to learn the drug action process in two stages: ① the drug–target binding stage, and ② the intracellular signal propagation stage within the protein. The three-dimensional protein structure was represented as an atom-level graph, and an attention mechanism was applied to enable the model to learn important signaling pathways. Through this approach, the AI analyzes not only the drug binding signal but also the internal signaling pathways of the protein to predict whether the protein becomes activated. As a result, the model significantly improved the prediction performance of drug activity even in proteins with complex structures that existing models struggled to analyze. Importantly, the model does not simply output “active” or “inactive.” It also presents the key internal signaling pathways that form the basis of its predictions, overcoming the limitations of so-called “black-box AI.” <Schematic diagram of drug activity prediction and mechanism interpretation using the GPCRact artificial intelligence model> This represents an important advance, as it allows researchers to interpret and verify predictions while simultaneously improving the reliability and efficiency of drug discovery. In the future, the model is expected to serve as a precision drug discovery AI platform capable of predicting not only whether drugs bind to GPCRs but also whether they truly activate them in various diseases targeting GPCRs. <AI-generated image to help illustrate the research> Professor Gwan-Su Yi explained, “Allosteric structural change refers to a phenomenon in which a drug binds to one part of a protein and its influence propagates internally, altering the function of other regions,” adding, “The key contribution of this research is incorporating this operational principle into deep learning.” He further noted, “We plan to expand the model to various proteins and ultimately develop technologies capable of predicting cellular and whole-body responses.” Ph.D candidate Hyojin Son participated as the first author in this study. The paper was published on January 15 in the international journal Briefings in Bioinformatics, one of the leading journals in the field of bioinformatics. ※ Paper title: “GPCRact: a hierarchical framework for predicting ligand-induced GPCR activity via allosteric communication modeling” DOI: https://doi.org/10.1093/bib/bbaf719 ※ Author information: Hyojin Son (KAIST, first author), Gwan-Su Yi (KAIST, corresponding author) This research was supported by the Basic Research Program for Individual Research funded by the Ministry of Science and ICT and the National Research Foundation of Korea (RS-2025-24533057).  

KAIST Team Led by Dong-won Lee Wins Grand Prize at..

  < (From Left) M.S candidate Dongwon Lee from School of Electrical Engineering, Ph.D candidate Jaehun Han from Graduate School of Quantum Science and Technology >   "Team Yangja-jorim," consisting of Dongwon Lee, Gyungjun Kim and Jaehun Han , has been honored with the Grand Prize at the '2026 2nd Global Quantum AI Competition.' The event was hosted and organized by NORMA, a specialized quantum computing company. This global competition was designed to expand hands-on experience with quantum cloud services and to discover next-generation talent in the field of quantum artificial intelligence. The event spanned approximately 70 days, beginning with the preliminary opening ceremony held at Korea University’s Hana Square on December 17 last year. The final winners were announced during an awards ceremony held at NORMA's headquarters on the 27th of last month. The competition attracted significant interest from quantum technology talent worldwide, including university students, developers, and researchers. A total of 137 teams participated in the preliminaries, with the top 10 teams advancing to the finals—a competitive ratio of approximately 13.7 to 1. < An acquaintance attended the awards ceremony of the 2nd Global Quantum AI Competition to accept the prize on behalf of the team. >   In the final round, participants were presented with four generative problems utilizing the Quantum Circuit Born Machine (QCBM) model. To overcome the current limitations of quantum machine learning, the contestants were tasked with designing and validating Quantum-Classical Hybrid Generative AI models that integrate classical techniques. Notably, the final problem provided an opportunity to verify the proposed methods using a real Quantum Processing Unit (QPU) from Rigetti Computing, a leading global quantum computing firm. The judging process employed a double-blind system, where the identities of both evaluators and participants remained undisclosed to ensure maximum fairness and credibility. "Through this competition, we were able to explore the research potential of the quantum AI field more deeply," said KAIST's Team Yangja-jorim in their acceptance speech. "We hope to continue contributing to the advancement of quantum technology through consistent research and new challenges."      

Professor Kuk-Jin Yoon’s Research Team at the Depa..

  <Professor Kuk-Jin Joon from Department of Mechanical Engineering>   Professor Kuk-Jin Yoon’s research team from our university’s Department of Mechanical Engineering has once again demonstrated its overwhelming academic prowess by having a total of 10 papers accepted as lead authors at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026). CVPR is the most influential international conference in the fields of artificial intelligence and visual intelligence. Since its inception in 1983, it has selected outstanding research through a rigorous peer-review process every year. For CVPR 2026, a total of 16,092 papers were submitted worldwide, with 4,090 accepted, resulting in a competitive acceptance rate of approximately 25.42％. Achieving 10 accepted papers as lead or corresponding authors from a single laboratory is regarded as an exceptionally rare and world-class feat. Professor Kuk-Jin Yoon’s team conducts extensive research with the ultimate goal of achieving human-level visual intelligence. The papers accepted this year cover cutting-edge topics in computer vision, including: Event camera-based technologies Perception technologies for autonomous driving AI optimization and adaptation techniques This achievement follows the team's remarkable success at ICCV 2025 last year, where they published 12 papers as lead/corresponding authors. The results at CVPR 2026 further solidify the laboratory's position as a global hub for pioneering computer vision research. The research team plans to continue contributing to the advancement of future AI technologies by tackling challenging research that transcends the limitations of existing methods. Meanwhile, CVPR 2026 is scheduled to be held in Denver, Colorado, USA, from June 3 to June 7. <CVPR 2026 (Denver, USA)>      

Earth’s Safety Limit Already Exceeded… Carbon Emis..

  <(From Left) Professor Haewon McJeon, Dr. Paul Wolfram>     Earth is not infinite. Pollution beyond certain levels threatens the climate and ecosystems. To prevent this, scientists have proposed “Planetary Boundaries,” defining the safe operating limits of the Earth system. A KAIST research team recalculated climate change and nitrogen pollution using the same standard and found that current carbon emissions already exceed the planet’s sustainable limit by more than double. KAIST (President Kwang Hyung Lee) announced on the 6th of March that Professor Haewon McJeon of the Graduate School of Green Growth and Sustainability, in collaboration with Dr. Paul Wolfram’s team at the Pacific Northwest National Laboratory (PNNL) of the U.S. Department of Energy, recalculated the carbon dioxide emission boundary using an annual emissions (flow) framework rather than the traditional cumulative carbon stock framework. Until now, climate change has been evaluated based on how much CO₂ accumulates in the atmosphere (stock). In contrast, nitrogen and phosphorus pollution have been assessed based on how much is emitted each year (flow). Because these problems were measured using different metrics, it was difficult to fairly compare their relative severity. The research team therefore recalculated carbon emissions using the same annual emissions framework used for nitrogen pollution. Based on the condition of limiting the rise in global average temperature to within 1.5°C, the analysis showed that the Earth’s safe limit for annual CO₂ emissions is approximately 4–17 gigatons (Gt CO₂ per year). However, humanity’s current annual emissions amount to about 37 gigatons (Gt CO₂ per year). This level exceeds the Earth’s safe operating space by more than twofold. Professor Haewon McJeon stated, “When carbon emissions are compared using the same framework as nitrogen pollution, the severity of climate change becomes much clearer,” adding, “This study helps place different environmental problems on the same analytical basis, which can contribute to setting clearer policy priorities.” <Comparative Measurement of Planetary Boundaries and Proposal for Flow-Based Carbon Emission Limits> <Scope and Sensitivity of Flow-Based Carbon Emission Limits> He further emphasized, “The need for integrated strategies that simultaneously consider carbon, nitrogen, and phosphorus pollution is growing,” adding that global efforts toward decarbonization must accelerate further. The study was jointly led by Professor Haewon McJeon and Dr. Paul Wolfram as co-corresponding authors, with Hassan Niazi, Page Kyle, and other researchers from PNNL participating as collaborators. The research results were published on February 16 in the international journal Nature Sustainability. ※ Paper title: “Ensuring consistency between biogeochemical planetary boundaries” DOI: https://doi.org/10.1038/s41893-026-01770-6 This research was supported by the project “Development of an AI-Based Next-Generation Integrated Assessment Model for Climate–Human Interactions” funded by the Ministry of Science and ICT and the National Research Foundation of Korea. In a Science commentary published on March 5 titled “Thirty-six solutions to stabilize Earth’s climate,” Professor McJeon revisited the progress of climate technologies over the past 20 years. He pointed out that although humanity has possessed many of the necessary technologies, they have not been implemented quickly enough, allowing the climate crisis to intensify. He also emphasized that the pace of decarbonization must accelerate to achieve carbon neutrality. ※ Commentary: “Thirty-six solutions to stabilize Earth’s climate” Link: https://doi.org/10.1126/science.aed5212    

Designing the Heart of Hydrogen Cars with AI... De..

  <(From left) KAIST Ph.D. Candidate HyunWoo Chang, Professor EunAe Cho. (Top, from left) Seoul National University Professor Won Bo Lee, Dr. Jae Hyun Ryu.>   In the era of climate crisis, hydrogen vehicles are emerging as an alternative for eco-friendly mobility. However, the fuel cell, known as the ‘heart of the hydrogen car,’ still faces limitations of high cost and short lifespan. The core cause is the platinum catalyst. While it is a decisive material for generating electricity, the reaction is slow, performance degrades over time, and manufacturing costs are high. Korean researchers have presented a clue to solving this difficult problem. KAIST announced on February 26th that the research team led by Professor EunAe Cho of the Department of Materials Science and Engineering, together with the team of Professor Won Bo Lee of the School of Chemical and Biological Engineering at Seoul National University, has developed a technology that predicts the ‘atomic arrangement’ tendency of catalysts using artificial intelligence (AI). This technology is akin to calculating beforehand which combination is advantageous for completing a puzzle before putting it together. By having AI calculate the arrangement speed of metal atoms first, it has become possible to efficiently design catalysts with better performance. The core of this research is that ‘AI revealed the fact that zinc plays a decisive role in the platinum-cobalt atomic arrangement.’ <Schematic diagram of AI-based atomic alignment prediction> Despite the high performance of existing platinum-cobalt (Pt-Co) alloy catalysts, very high-temperature heat treatment was required to create the ‘intermetallic (L1₀)’ structure, where atoms are regularly arranged. In this process, particles would clump together, or the structure would become unstable, posing limitations for actual fuel cell application. To solve this problem, the research team introduced machine learning-based quantum chemistry simulations. Through AI, they precisely predicted how atoms move and arrange themselves inside the catalyst. As a result, they discovered that zinc (Zn) acts as a mediating element that promotes atomic arrangement. The principle is that when zinc is introduced, atoms find their places more easily, forming a more sophisticated and stable structure. In other words, AI has found the ‘optimal path for atomic arrangement creation’ in advance. < Synthesis process of Zinc-introduced Platinum-Cobalt catalyst> The zinc-platinum-cobalt catalyst, synthesized based on AI predictions, secured both higher activity and superior long-term durability compared to commercial platinum catalysts. This is a case proving that the ‘virtual blueprint’ calculated by artificial intelligence can be implemented as a high-performance catalyst in an actual laboratory. In particular, this technology is expected to contribute to extending catalyst lifespan and reducing manufacturing costs across core carbon-neutral industries, such as hydrogen passenger cars, hydrogen trucks requiring long-distance operation, hydrogen ships, and energy storage systems (ESS). < Conceptual diagram of AI-based catalyst development (AI-generated image) > Professor EunAe Cho stated, “This research is a case of utilizing machine learning to predict the atomic arrangement tendency of catalysts in advance and implementing this through actual synthesis,” and added, “AI-based material design will become a new paradigm for the development of next-generation fuel cell catalysts.” Ph.D. Candidate HyunWoo Chang from KAIST’s Department of Materials Science and Engineering and Dr. Jae Hyun Ryu from Seoul National University’s School of Chemical and Biological Engineering participated as co-first authors in this research. The research results were published on January 15, 2026, in ‘Advanced Energy Materials,’ a world-renowned academic journal in the energy materials field. ※ Paper Title: Machine Learning-Guided Design of L1₀-PtCo Intermetallic Catalysts: Zn-Mediated Atomic Ordering, DOI: https://doi.org/10.1002/aenm.202505211 This research was conducted with the support of the National Research Foundation of Korea’s Nano & Material Technology Development Program and the Korea Institute of Energy Technology Evaluation and Planning’s Energy Innovation Research Center for Fuel Cell Technology.    

The long standing commercialization challenge of l..

  <(From Upper Left) Professor Nam Soon Choi, Professor Seungbum Hong, Professor Sang Kyu Kwak, (From Below Left) y Jeong-A. Lee, Haneul Kang, Yoonhan Cho, Seong Hyeon Kweon, Seonghyun Kim>     As the electric vehicle era enters full scale, demand is increasing for batteries that can travel farther and last longer. Lithium-metal batteries have been attracting attention as a next-generation technology capable of surpassing the capacity limits of existing lithium-ion batteries. However, during the charging process, needle-shaped crystals called “dendrites” grow, shortening battery life and increasing the risk of fire, which has been identified as the biggest obstacle to commercialization. A Korean research team has developed a key technology that can solve this challenge. KAIST announced on the 24th that the research team led by Prof. Nam-Soon Choi from the Department of Chemical and Biomolecular Engineering and Prof. Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with Prof. Sang Kyu Kwak’s team at Korea University, has developed a technology that resolves the most critical challenge of lithium-metal batteries, “interfacial instability,” at the electronic structure level. Interfacial instability refers to the phenomenon in which the boundary between the electrode and electrolyte cannot be maintained uniformly during charging and discharging. As a result, lithium grows in needle-like dendrites, which leads to reduced battery cyclability, internal short circuits, and increased Thermal instability. This has been the fundamental cause preventing the commercialization of lithium-metal batteries. The research team implemented an “intelligent protective layer” that allows lithium ions to move stably along the electrode surface by adding thiophene to the battery electrolyte. This protective layer has the characteristic that its electronic structure rearranges itself. Like a smart traffic system that adjusts lanes according to traffic flow, the charge distribution inside the protective layer flexibly changes whenever lithium ions move, creating optimal pathways. The research team identified this mechanism through density functional theory (DFT) simulations and confirmed much higher stability compared to existing commercial additives. As a result, they succeeded in effectively suppressing dendrite growth even under fast-charging conditions and significantly extending battery lifespan. In addition, the research team directly observed the inside of the battery at the nanometer scale using in-situ atomic force microscopy (AFM). Even under high current conditions, they confirmed that lithium was deposited and removed uniformly on the surface, thereby verifying mechanical stability. This technology can be applied to various cathode materials currently widely used, including lithium iron phosphate (LiFePO₄), lithium cobalt oxide (LiCoO₂), and lithium nickel–cobalt–manganese oxide LiNixCoyMn1-x-yO₂). Because it is not limited to a specific battery type and can be broadly applied across existing electric vehicle battery systems, it is expected to have significant industrial impact. This achievement is meaningful in that it presents a breakthrough capable of fundamentally solving the ultra-fast charging problem—which has been the biggest barrier to lithium-metal battery commercialization—by simultaneously enabling fast charging within 12 minutes and high-current operation exceeding 8 mA/cm². 8 mA/cm² refers to a level at which 8 milliamperes of current flow per square centimeter of battery electrode area. In lithium-metal battery research, even around 4 mA/cm² is typically considered a “high current” condition, so this represents more than twice that level and corresponds to operating conditions close to real-world electric vehicle fast charging, rapid acceleration, and high-power driving. Through this breakthrough, the technology is expected to be applied to various future industries requiring high-performance batteries, including ultra-long-range electric vehicles, urban air mobility (UAM), and next-generation high-density energy storage systems. Prof. Nam-Soon Choi stated, “This research is not simply a material improvement but an achievement that solves the fundamental problem of batteries by designing the electronic structure,” adding, “It will become a core foundational technology for next-generation electric vehicle batteries that simultaneously achieve fast charging and long lifespan.” This study was conducted by Jeong-A. Lee, Haneul Kang, Yoonhan Cho, Seong Hyeon Kweon, Seonghyun Kim, Syed Azkar UI Hasan, Minju Song, Saehun Kim, Eunji Kwon, Samuel Seo, Kyoung Han Ryu, Rama K. Vasudevan, Sang Kyu Kwak, Seungbum Hong, and Nam-Soon Choi, and was published on February 2 in the internationally renowned materials and energy journal InfoMat. Paper title: Conjugation-mediated and polarity-switchable interfacial layers for fast cycling of lithium-metal batteries DOI: http://doi.org/10.1002/inf2.70126 Meanwhile, this research was conducted with support from Hyundai Motor Company and the mid-career researcher program of the National Research Foundation of Korea.

Developing Technology to Become the Joker in The D..

  <(From left) Ph.D. candidate Taewoong Kang, Ph.D candidate Junha Hyung, Professor Jaegul Choo, and Ph.D. candidate Minho Park (From top right square, from left), Ph.D. candidate Kinam Kim, Seoul National University undergraduate researcher Dohyeon Kim>   What if, while watching The Dark Knight, you weren't just observing the Joker on screen, but actually seeing Gotham City through his eyes? The video technology that allows viewers to experience the world through a character's perspective, rather than as a mere observer, is becoming a reality. Researchers at our university have developed a new AI model that generates first-person viewpoint videos from standard footage. KAIST announced on February 23rd that Professor Jaegul Choo’s research team at the Kim Jaechul Graduate School of AI has developed 'EgoX,' an AI model that utilizes observer-perspective (exocentric) video to precisely generate the scenes that a person in the video would actually be seeing. With the rapid advancement of Augmented Reality (AR), Virtual Reality (VR), and AI robotics, the importance of "egocentric video"—which captures scenes as one directly sees them—is growing. However, obtaining high-quality first-person footage previously required users to wear expensive action cameras or smart glasses. Furthermore, there were significant technical limitations in naturally converting existing standard (third-person or exocentric) video into a first-person perspective. A key feature of this technology is that it goes beyond simply rotating the screen; it comprehensively understands the person's position, posture, and the 3D structure of the surrounding space to reconstruct the first-person viewpoint. < Example of converting a third-person perspective video into a first-person perspective video > Existing technologies often only converted still images or required footage from four or more cameras. Additionally, they frequently suffered from awkward visual artifacts in videos with complex lighting or rapid movement. In contrast, EgoX can generate high-quality first-person video from just a single third-person video source. Specifically, the research team succeeded in realistically implementing natural shifts in vision—such as when a person turns their head—by precisely modeling the correlation between head movement and the actual field of view. This technology demonstrated stable performance across various daily scenarios, including cooking, exercising, and working, without being limited to specific environments. It is being evaluated as a breakthrough that opens new possibilities for securing high-quality first-person data from existing video archives without the need for wearable devices. EgoX is expected to have a significant impact across various industries. In the fields of AR, VR, and the Metaverse, it can maximize user experience by transforming standard videos into immersive content that makes users feel as if they are experiencing the scene firsthand. Furthermore, it is projected to contribute to the fields of robotics and AI training by serving as core data for "Imitation Learning," where robots learn by watching human actions. New types of video services, such as switching sports broadcasts or vlogs to the perspective of the athlete or the protagonist, are also anticipated. < EgoX technology that converts a third-person perspective into a first-person perspective (AI-generated image) > Distinguished Professor Jaegul Choo stated, "This research is significant in that AI has moved beyond simple video conversion to learning and reconstructing human 'vision' and 'spatial understanding.' We expect an environment to open up where anyone can create and experience immersive content using only previously recorded videos." He added, "KAIST will continue to secure global competitiveness in the field of generative AI-based video technology." This research was led by first authors Taewoong Kang, Kinam Kim, and Dohyeon Kim . The paper was pre-released on arXiv on December 9, 2025, garnering significant attention from AI industry giants like NVIDIA and Meta, as well as academia. It is scheduled for official presentation at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), an international academic conference to be held in Colorado, USA, on June 3, 2026. Paper Title: EgoX: Egocentric Video Generation from a Single Exocentric Video Paper Link: https://keh0t0.github.io/EgoX/   Meanwhile, this research was supported by the Ministry of Science and ICT through the National Research Foundation of Korea's individual basic research project, "Research on User-Centered Content Generation and Editing Technology through Generative AI," and the Supercomputer No. 5 High-Performance Computing-based R&D Innovation Support project, "Research on Video Filming Viewpoint Conversion Based on Diffusion Models."  

KAIST Overcomes Limitations of Existing Image Sens..

  <(From Left) Ph.D candidate Chanhyung Park from Electrical Engineering, Jaehyun Jeon from Department of Physics, Professor Min Seok Jang from Electrical Engineering>   Smartphone cameras are becoming smaller, yet photos are becoming sharper. Korean researchers have elevated the limits of next-generation smartphone cameras by developing a new image sensor technology that can accurately represent colors regardless of the angle at which light enters. The team achieved this by utilizing a “metamaterial” that designs the movement of light through structures too small to be seen with the naked eye. KAIST (President Kwang Hyung Lee) announced on the 12th of February that a research team led by Professor Min Seok Jang of the School of Electrical Engineering, in collaboration with Professor Haejun Chung’s team at Hanyang, has developed a metamaterial-based technology for image sensors that can stably separate colors even when the angle of light incidence varies. Conventional smartphone cameras capture images by concentrating light into a small lens. However, as camera pixels become extremely small, lenses alone struggle to gather sufficient light. To address this, the Nanophotonic Color Router was introduced. Instead of concentrating light through a lens, this technology uses microscopic structures invisible to the eye to precisely separate incoming light by color. By designing the pathways through which light travels, this metamaterial-based structure accurately divides light into red (R), green (G), and blue (B). Samsung Electronics has already demonstrated the commercialization potential of this technology by applying it to actual image sensors under the name “Nano Prism.” Theoretically, stacking multiple layers of extremely fine nanostructures enables greater light collection and more accurate color separation. <Nanophotonic color router technology that works reliably even under oblique incidence conditions (AI-generated image)> However, existing Nanophotonic Color Routers had limitations. While they functioned well when light entered vertically, their performance deteriorated significantly—or colors mixed—when light entered at an angle, as is common in smartphone cameras. This issue, known as the “oblique incidence problem,” has been considered a critical challenge that must be resolved for real-world product applications. The research team first investigated the root cause of this issue. They found that previous designs were overly optimized for vertically incident light, causing performance to drop sharply even with slight changes in the angle of incidence. Since smartphone cameras receive light from various angles, maintaining performance under angular variation is essential. Instead of manually designing the structure, the team adopted an “inverse design” approach, which allows the computer to autonomously determine the optimal structure. Through this method, they derived a color router design capable of stable color separation even when the angle of incoming light changes. As a result, whereas previous structures nearly failed when light was tilted by about 12 degrees, the newly designed structure maintained approximately 78％ optical efficiency within a ±12-degree range, demonstrating stable color separation performance. In other words, the technology reaches a level suitable for practical smartphone usage environments. <Nanophotonic color router robust to oblique incidence> The team further analyzed performance variations by considering factors such as the number of metamaterial layers, design conditions, and potential fabrication errors. They also systematically defined the limits of robustness against changes in the angle of incidence. This study is particularly meaningful in that it presents design criteria for color routers that reflect realistic image sensor environments. Professor Min Seok Jang of KAIST stated, “This research is significant in that it systematically analyzes the oblique incidence problem, which has hindered the commercialization of color router technology, and proposes a clear solution direction,” adding, “The proposed design methodology can be extended beyond color routers to a wide range of metamaterial-based nanophotonic devices.” In this study, KAIST undergraduate student Jaehyun Jeon and doctoral candidate Chanhyung Park participated as co-first authors. The research findings were published on January 27 in the international journal Advanced Optical Materials. ※ Paper title: “Inverse Design of Nanophotonic Color Router Robust to Oblique Incidence” DOI: https://doi.org/10.1002/adom.202501697 ※ Authors: Jaehyun Jeon (KAIST, first author), Chanhyung Park (KAIST, first author), Doyoung Heo (KAIST), Haejun Chung (Hanyang University), Min Seok Jang (KAIST, corresponding author) This research was supported by the Ministry of Trade, Industry & Energy (Korea Institute for Advancement of Technology, Korea Semiconductor Research Consortium) under the project “Design Technology of Meta-Optical Structures for Next-Generation Sensors,” by the Ministry of Science and ICT (National Research Foundation of Korea) under the projects “Development of Full-Color Micro LED Devices and Panels Based on Beam-Steerable High-Color-Purity Meta Color Conversion Layers” and “Development of a Real-Time Zero-Energy Argos-Eye Metasurface Network Computing with All Properties of Light,” and by the Ministry of Culture, Sports and Tourism (Korea Creative Content Agency) under the project “International Joint Research for Next-Generation Copyright Protection and Secure Content Distribution Technologies.”

KAIST Uses Sandpaper to Polish Semiconductors… Ope..

  <(From Left) Dr. Sukkyung Kang, Professor Sanha Kim from Department of Mechanical Engineering>      The performance and stability of smartphones and artificial intelligence (AI) services depend on how uniformly and precisely semiconductor surfaces are processed. KAIST researchers have expanded the concept of everyday “sandpaper” into the realm of nanotechnology, developing a new technique capable of processing semiconductor surfaces uniformly down to the atomic level. This technology demonstrates the potential to significantly improve surface quality and processing precision in advanced semiconductor processes such as high-bandwidth memory (HBM). KAIST (President Kwang Hyung Lee) announced on the 11th of February that a research team led by Professor Sanha Kim of the Department of Mechanical Engineering has developed a “nano sandpaper” that utilizes carbon nanotubes—tens of thousands of times thinner than a human hair—as abrasive materials. This technology enables more precise surface processing than existing semiconductor manufacturing processes, while also reducing environmental burdens generated during fabrication, presenting a new planarization technique. < Nano Sandpaper AI-Generated Image >  Although sandpaper is a familiar tool used to smooth surfaces by rubbing, it has been difficult to apply it to fields such as semiconductors, where extremely precise surface processing is required. This limitation arises because conventional sandpaper is manufactured by attaching abrasive particles with adhesives, making it difficult to uniformly secure extremely fine particles. To overcome such limitations, the semiconductor industry has adopted a planarization process known as chemical mechanical polishing (CMP), which uses a chemical slurry in which abrasive particles are dispersed in liquid. However, this method requires additional cleaning steps and generates large amounts of waste, making the process complex and environmentally burdensome. To address these issues, the research team extended the concept of sandpaper to the nanoscale. By vertically aligning carbon nanotubes, fixing them inside polyurethane, and partially exposing them on the surface, they implemented a “nano sandpaper.” This structure structurally suppresses abrasive detachment, eliminating concerns about surface damage and maintaining stable performance even after repeated use. The nano sandpaper developed in this study achieves an abrasive density approximately 500,000 times higher than that of the finest commercially available sandpaper. The precision of sandpaper is expressed in terms of “abrasive density (grit number),” which indicates how densely abrasive particles are arranged on the surface. While everyday sandpaper typically ranges from 40 to 3000 grit, the nano sandpaper exceeds 1,000,000,000 grit. Through this extremely dense structure, surfaces could be processed with precision down to several nanometers—equivalent to the thickness of only a few atoms. The effectiveness of the nano sandpaper was confirmed through experiments. Rough copper surfaces were polished to a smoothness at the nanometer level, and in semiconductor pattern planarization experiments, the technique reduced dishing defects by up to 67％ compared with conventional CMP processes. Dishing defects refer to the phenomenon in which the center of interconnect lines becomes recessed, a major defect affecting the performance and reliability of advanced semiconductors such as HBM. In particular, because the abrasive materials are fixed on the sandpaper surface, the technology does not require continuous supply of slurry solutions as in conventional processes. This reduces cleaning steps and eliminates waste slurry, presenting the possibility of transitioning semiconductor manufacturing toward more environmentally friendly processes. < Nano Sandpaper Schematic Diagram >  < Detailed Image of Nano Sandpaper > The research team expects that this technology can be applied to advanced semiconductor planarization processes such as HBM used in AI servers, as well as to hybrid bonding processes, which are gaining attention as next-generation semiconductor interconnection technologies. The study is also significant in that it expands the everyday concept of sandpaper into nano-precision processing technology, suggesting the possibility of securing core technologies required for semiconductor manufacturing. Professor Sanha Kim stated, “This is an original study demonstrating that the everyday concept of sandpaper can be extended to the nanoscale and applied to ultra-fine semiconductor manufacturing,” adding, “We hope this technology will lead not only to improved semiconductor performance but also to environmentally friendly manufacturing processes.” In this study, Dr. Sukkyung Kang of the Department of Mechanical Engineering participated as the first author. The research was recognized for its excellence by receiving the Gold Prize (1st place) in the Mechanical Engineering Division at the 31st Samsung Human Tech Paper Award, hosted by Samsung Electronics. The findings were published online on January 8, 2026, in the international journal Advanced Composites and Hybrid Materials (IF 21.8). ※ Paper title: “Carbon nanotube sandpaper for atomic-precision surface finishing” DOI: https://doi.org/10.1007/s42114-025-01608-3 This research was supported by the National Research Foundation of Korea (Mid-Career Researcher Program; Ministry of Science and ICT, NRF, RS-2025-00560856), the Glocal Lab Program (Ministry of Education, NRF, RS-2025-25406725), the InnoCORE Program (Ministry of Science and ICT, NRF, N10250154), and the KAIST Up Program.