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"Development of 'ADvisor', an AI that Predicts Instagram Advertising Performance in Advance"
<(Bottom from left) M.S candidate Gyurim Hwang, M.S candidate Yeongho Kim, Ph.D. candidate Kyungho Kim, Ph.D.candidate Jongha Lee, M.S candidate Yeonje Choi (Top from left) Undergraduate student Sejin Chung, Researcher Hongseok Lee, Researcher Myeong Ho Song, Ph.D. candidate Sunwoo Kim, M.S candidate Juyeon Kim, Professor Kijung Shin>
Social media advertising usually requires running multiple ad drafts in practice before determining which ad is effective. Because of this, testing advertisements demands significant time and costs. Furthermore, the criteria for an effective advertisement vary greatly by brand. While some brands prefer person-centered advertisements, others receive better responses from advertisements that emphasize actual usage scenes. However, these effective advertising strategies for each brand are often not clearly defined in the field, which has limited the technology to systematically reflect them and predict advertising performance.
To solve this problem, a research team led by Professor Kijung Shin at KAIST, in collaboration with the AI marketing company MADUP, developed 'ADvisor', an AI technology that predicts advertising performance for each brand.
ADvisor utilizes a generative vision-language model that understands images and text simultaneously to find different advertising success criteria for each brand and predict advertising effectiveness based on them. To achieve this, it not only analyzes the characteristics of the brand but also considers advertising data from other brands with similar tendencies for new brands that do not have sufficient advertising data to derive advertising strategies. Through this process, it can identify distinct advertising success criteria for each brand; for instance, a "strong headline phrase" is analyzed as an important criterion for a specific fashion brand, while "logo exposure" acts as a key element for another brand. Afterward, ADvisor evaluates the advertisement based on the derived criteria for each brand, reviews the evaluation results on its own, and repeatedly compensates for deficiencies to make the final prediction.
The research team verified the technology's performance using data from 10 brands in the beauty, fashion, and platform sectors collected through actual marketing campaigns. As a result, ADvisor recorded up to 7.2% higher performance compared to existing AI advertising prediction models. In particular, in an online A/B test conducted in a real Instagram advertising environment, it achieved an average of 27% better performance in key indicators such as click-through rate (CTR), cost per click (CPC), and return on ad spend (ROAS) than advertisements selected by field marketing experts, proving that it can be utilized in actual marketing decision-making.
Professor Kijung Shin stated, "Predicting advertising performance in advance is the first step for effective advertisement production," adding, "In the future, we will develop our research in a direction where AI directly generates and optimizes advertisements tailored to brand characteristics."
The study, in which Ph.D candidate Kyungho Kim and M.S candidate Yeonje Choi from the KAIST Kim Jaechul Graduate School of AI participated as co-first authors, was published online on April 18 in the Industry Track of ACL 2026, one of the most prestigious international academic conferences in the field of natural language processing. It has been accepted as an oral presentation paper and is scheduled to be presented in the United States this coming July.
※ Paper Title: Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs ※ Paper Link: https://openreview.net/forum?id=il84gAzAxx
Meanwhile, this research is an achievement of the project 'EntireDB2AI: Deep Representation Learning and Prediction Source Technology and Software Development Utilizing Entire Relational Databases Comprehensively', supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP).
2026.06.01 View 245 -
Development of a Virtual AI Testbed Capable of Performance Verification Before Building Massive AI Servers
< From left: Professor Jongse Park , M.S candidate Jaehong Cho, M.S candidate Hyunmin Choi, Professor Brandon Reagen ISPASS >
Operating Large Language Model (LLM) services like ChatGPT requires a server infrastructure on the scale of tens of thousands of units. However, constructing actual equipment every time a new AI semiconductor or system architecture needs to be verified incurs massive costs and time. A research team at our university has developed a ‘virtual testbed’ that can pre-verify performance and efficiency inside a computer before building an actual large-scale AI server.
KAIST announced on May 29th that the research on a Large Language Model (LLM) serving infrastructure simulator (virtual testing software) developed by Professor Jongse Park’s research team in the School of Computing won the Best Paper Award at ‘ISPASS 2026 (IEEE International Symposium on Performance Analysis of Systems and Software),’ a world-renowned conference in the field of computer system performance analysis.
‘LLMServingSim 2.0,’ developed by the research team, is a simulation platform capable of virtually analyzing various hardware and software combinations in complex AI service environments. Researchers and developers can freely experiment with various design options and verify performance without having to directly build expensive, large-scale server infrastructures.
< LLMServingSim 2.0 is workload >
In particular, this technology is drawing attention because it goes beyond the existing Graphics Processing Unit (GPU)-centric environment to support diverse hardware environments, including Neural Processing Units (NPUs), which are rising as next-generation AI semiconductors, and Processing-In-Memory (PIM, a semiconductor technology that performs operations inside the memory).
In other words, it is a technology that allows future-oriented AI semiconductors that have not yet been commercialized to be tested in advance within a virtual datacenter environment. Through this, it is possible to replicate and analyze inside a computer how much the service speed improves, how much power consumption is reduced, and whether it operates stably even in a server environment scaled to tens of thousands of units when a specific semiconductor is applied.
In addition, it reproduces complex operations that occur during actual AI service operations—such as data processing, request distribution, and memory utilization—at the system level, enabling performance evaluations that are close to reality. Notably, it can even analyze disaggregated infrastructure environments where multiple server resources are separated and connected for use, showing great potential for utilization in next-generation AI datacenter research.
This simulator is expected to be widely utilized not only by researchers but also by LLM service companies and AI semiconductor startups to design and optimize next-generation AI infrastructures. This is because it can rapidly verify new AI semiconductors or service architectures prior to actual construction, thereby significantly reducing the cost and time of AI infrastructure development.
< Research Image (AI-generated image) >
Professor Jongse Park said, “The competitiveness of AI services is determined not only by the model itself but also by the infrastructure technology that operates it stably and efficiently.” He added, “We hope this simulator will serve as an important foundation for researchers and the industry to develop next-generation AI infrastructures faster and more efficiently.”
This research was led by M.S candidate Jaehong Cho and Hyunmin Choi in the School of Computing as co-first authors. Following their Best Paper Award at the 2024 IISWC (IEEE International Symposium on Workload Characterization), the research team won the Best Paper Award again at this ISPASS 2026, proving their research competitiveness in the field of AI infrastructure once more.
※ Paper Title: LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure, DOI: 10.1109/ISPASS69572.2026.00012 (Authors: Jaehong Cho, Hyunmin Choi, Guseul Heo, Jongse Park) ※ Open Source Link: https://llmservingsim.ai/ Meanwhile, this research was conducted with support from the Ministry of Science and ICT (MSIT), the Institute for Information & Communications Technology Planning & Evaluation (IITP, No. RS-2024-00396013), the Electronics and Telecommunications Research Institute (ETRI, No. RS-2025-02305453), and SK hynix.
2026.05.29 View 365 -
Clearing the Expressway for Bubble Blockages to Achieve High-Efficiency Green Hydrogen Production
< (From left) Ph.D candidate Jaeho Byeon, Ph.D candidate Minkyeong Ban, Professor Jinwoo Lee, Dr. Sungjun Kim, Professor Jang Yong Lee>
As the global transition toward carbon neutrality accelerates, "water electrolysis"—a technology that splits water electrically to produce clean hydrogen—is drawing significant attention. However, a major limitation has been the decline in efficiency caused by bubbles formed during the electrolysis process that block the pathways. A domestic research team has resolved this challenge by developing an innovative technology that rapidly discharges bubbles and boosts hydrogen production efficiency, much like clearing an expressway through a heavily congested road.
KAIST announced on May 28th that a research team led by Professor Jinwoo Lee from the Department of Chemical and Biomolecular Engineering, in collaboration with a research team led by Dr. Sungjun Kim from KRICT (President Suk-min Shin) and a research team led by Professor Jang Yong Lee from Konkuk University (President Jong-phil Won), has departed from the conventional method of simply increasing catalytic activity itself. Instead, they have successfully secured both water electrolysis performance and stability simultaneously by newly designing a "pathway" inside the catalyst layer through which water and gas pass.
< Development of World-Class Anion Exchange Membrane Water Electrolysis via Carbon-Induced Ru-C Bonds and Catalyst Layer Structural Design >
Using paper-thin 2D mesoporous carbon (a thin carbon structure with numerous nanoscale pores) nanosheets, the research team created a low-tortuosity structure where materials can move without obstruction. Simply put, they implemented a "highway-like pathway" inside the catalyst layer through which water and gas can pass rapidly, instead of a narrow and complex alleyway.
Furthermore, ruthenium (Ru) nanoclusters (ultrafine metal particles several nanometers in size) were stably anchored onto the defect-introduced carbon surface to accelerate the hydrogen evolution reaction rate. Simultaneously, the interface structure was controlled to prevent catalyst degradation even during long-term operation.
Through this technology, it was confirmed that bubbles generated during the water electrolysis process were rapidly discharged without accumulating inside the catalyst layer, and a stable reaction was maintained even under extreme environments with high current density.
As a result, the technology recorded a world-class performance of 17.1 A cm⁻² at 80°C, vastly exceeding the 2026 target set by the U.S. Department of Energy (DOE). This figure represents the amount of current flowing per unit area; a higher value signifies that more hydrogen can be produced faster.
In addition, it demonstrated practical industrial applicability by operating stably for over 1,000 hours even under a low noble metal loading condition (0.09 mgRu cm⁻²). This means that the amount of ruthenium, a precious metal used in the catalyst, has been significantly reduced, which can also enhance the economic viability of water electrolysis systems.
The core of this research lies not simply in making a "good catalyst," but in newly designing the pathway itself through which hydrogen is formed. In conventional water electrolysis devices, bubbles generated during the reaction process accumulate inside, blocking the flow of water and electricity, which leads to a degradation in performance. The research team solved this problem by changing the structure of the catalyst layer so that bubbles can exit rapidly.
This technology holds great significance as it opens the way to produce eco-friendly hydrogen more affordably and efficiently in the future. Hydrogen is currently attracting attention as a core clean energy source for the carbon-neutral era, but it has faced limitations due to high production costs and low system efficiency. In particular, conventional high-performance water electrolysis devices required large amounts of expensive noble metals, making large-scale commercialization difficult.
The research team explained that this technology demonstrates the potential to achieve high performance and stability with only a small amount of noble metals. It is expected to expand into various fields in the future, including large-scale green hydrogen production, eco-friendly power generation systems, hydrogen vehicles/eco-friendly mobility, and carbon-neutral industrial processes.
< 2D Mesoporous Catalyst Layer-Based Green Hydrogen Production Technology (AI-Generated Image) >
Professor Jinwoo Lee stated, "This research is a technology that improves water electrolysis efficiency by designing not only the catalyst itself but also the path through which energy flows. Since high-efficiency green hydrogen production is possible with only a small amount of noble metals, we expect to accelerate the commercialization of eco-friendly hydrogen production in the future."
In this study, PhD students Jaeho Byeon and Minkyeong Ban from the KAIST Department of Chemical and Biomolecular Engineering participated as co-first authors. The research findings were published online on May 22, 2026, in Joule, the world's leading academic journal in the energy field, and will be featured in the formal issue of Joule on September 16.
※ Paper Title: Outperforming water electrolysis through catalyst layer structuring with defective 2D mesoporous carbon, DOI: 10.1016/j.joule.2026.102478
※ Author Information: A total of 18 authors including Jaeho Byeon (KAIST, co-first author), Minkyeong Ban (KAIST, co-first author), Liangliang Xu (co-first author), Seunggeon Lee, Seongbeen Kim, Seonggyu Lee, Seongmin Shin, Donghyeok Son, Wonchul Park, Jinkyu Park, Hoyoung Kim, Dongyoon Woo, Seongseop Kim, Dong Young Chung, Jaewook Nam, Jang Yong Lee (Konkuk University, corresponding author), Sungjun Kim (KRICT, corresponding author), and Jinwoo Lee (KAIST, corresponding author).
This research was conducted with support from the National Research Foundation of Korea’s "AEM Water Electrolysis Technology Development" (RS-2024-00467234), the "Nano-Future Materials Source Technology Development" (RS-2023-00235596), the Ministry of Education’s "Ph.D. Student Research Support Project" (RS-2025-25424765), the Korea Research Institute of Chemical Technology (KS2522-10), and the Lotte Chemical Carbon Neutral Center.
2026.05.28 View 433 -
KAIST Develops AI Technology That Automatically Generates Sounds as If a “Jurassic Park” Dinosaur Were Actually Walking Toward You
<(From Left) Hyun-Bin Oh, Takida Yuhta, Uesaka Toshimitsu, Tae-Hyun Oh, Mitsufuji Yuki>
When people watch a scene in the film Jurassic Park where a giant dinosaur walks toward them, they naturally imagine a heavy, rumbling sound, as if the ground were shaking. This is because humans predict sound by considering not only the shape of an object, but also physical properties such as its size, weight, and speed of movement. However, existing video-to-audio generation AI mainly generates sound based on the category of objects or scene information in the video, and has not sufficiently reflected physical properties that vary depending on weight or speed.
KAIST (President Kwang Hyung Lee) announced on the 26th of May that a collaborative research team involving Professor Tae-Hyun Oh of the School of Computing, KAIST, together with joint researchers from POSTECH (President Sung Keun Kim) and Sony AI, has developed “PAVAS (Physics-Aware Video-to-Audio Synthesis),” an artificial intelligence (AI) technology that understands the physical situation in a video and generates more realistic sound.
<Concept Diagram of PAVAS (Physics-Aware Video-to-Audio Synthesis) Technology>
The key feature of this technology is that it is designed so that AI can infer invisible physical information such as the mass and velocity of objects in a video on its own. Ordinary videos do not provide exact numerical values for an object’s weight or speed, but the research team enabled AI to estimate them by analyzing the surrounding environment and movement context, and to reflect the results in the sound generation process.
In other words, the AI was designed to go beyond simply recognizing “what is visible” and to understand the physical cause of “why this sound should occur.”
As a result of technical validation, the research team’s AI generated sounds very similar to real-world environments in scenes involving physical interactions such as collisions or impacts between objects. In particular, it produced more realistic audio in which loudness and tone naturally changed when the mass and velocity of objects varied.
Recently, generative AI technologies that simultaneously generate video and audio have been advancing rapidly. Representative examples include Google’s “Veo 3” and ByteDance’s “Seedance 2.0.” However, in actual film, advertising, and game production sites, there is far greater demand for post-production work that adds sound effects suited to existing video scenes or supplements audio than for generating entirely new videos.
While existing commercial AI models have focused on generating video and audio together, PAVAS is differentiated by its ability to analyze the movement and collision characteristics of objects in a video and generate realistic sound effects that precisely match the scene.
<Comparison of Spectrograms Generated by Conventional Video-to-Audio Models and PAVAS>
The research team explained that this technology presents new possibilities in the field of “Physical AI,” or physically consistent generative AI. Physically consistent generative AI refers to AI that goes beyond simply producing plausible results and understands the laws of physics and causal relationships in the real world.
In the future, this technology is expected to provide more immersive user experiences in a wide range of fields, including the automation of content sound production, augmented reality (AR) and virtual reality (VR) content, the metaverse, and robotics simulation.
Professor Tae-Hyun Oh stated, “While existing generative AI has developed by increasing the scale of data and models, this research is meaningful in that it was designed so that AI directly understands physical quantities and causal relationships,” adding, “In the future, it can be expanded into a core foundational technology for next-generation multimodal AI that simultaneously understands and processes diverse types of information, including text, video, and speech.”
This study was led by POSTECH integrated M.S.-Ph.D. student Hyun-Bin Oh as the first author, with KAIST Professor Tae-Hyun Oh and Sony AI researchers Yuhta Takida, Toshimitsu Uesaka, and Yuki Mitsufuji participating as co-authors. This research was selected as an Oral presentation paper at CVPR 2026 (Computer Vision and Pattern Recognition 2026), the world’s most prestigious academic conference in the field of computer vision (image-based artificial intelligence technology), where only the top 0.88% of all papers are selected for oral presentation, recognizing the excellence of the work. The presentation is scheduled to take place on June 6.
※ Paper title: “PAVAS: Physics-Aware Video-to-Audio Synthesis,” DOI: https://arxiv.org/abs/2512.08282
This research was supported by the Mid-Career Research Program under the Basic Research Program of the Ministry of Science and ICT, the Pioneer Research Program for Future Converging Technology of the Ministry of Science, ICT and Future Planning, the AGI Program of the Ministry of Science and ICT, and the KAIST InnoCORE Program.
2026.05.27 View 491 -
Talking to AI Before Seeing a Doctor… KAIST Develops Technology to Support Initial Psychiatric Interviews
<(Front row, from right)Professor Uichin Lee, Professor Eunjoo Kim, Professor Tak Yeon Lee, (Back row, from left) M.S candidate Gyeongmin Na, Ph.D candidate Yugyeong Jung, Researcher Hyangkyeong Oh, M.S candidate Jae Young Choi, Ph.D candidate Hyun Seung Moon>
People often say that seeking psychiatric care can feel intimidating. Patients may feel burdened when they first open up about their emotional distress, while medical staff must accurately understand a patient’s extensive history and symptoms within limited consultation time. Korean researchers have developed artificial intelligence (AI) technology that supports the initial psychiatric interview process, the first step in psychiatric care.
KAIST (President Kwang Hyung Lee) announced on the 24th of May that a joint research team led by Professor Uichin Lee of the School of Computing and Professor Tak Yeon Lee of the Department of Industrial Design, together with Professor Eunjoo Kim’s team from the Department of Psychiatry at Gangnam Severance Hospital (President Yong-Wook Kim), has developed a large language model (LLM)-based technology to support initial psychiatric interviews.
This study was conducted in a way that allows patients to first talk with AI before meeting a doctor, helping them organize their symptoms and condition in advance.
<AI Interviewer System Overview Diagram>
<Ask-Evaluate-Check-Plan Conversation Flow>
The research team designed the system so that AI can adjust the flow of conversation according to patient responses. The AI analyzes patients’ answers in real time by comparing them with specialized medical knowledge in psychiatry and generates the key questions that should be asked next. In particular, this system goes beyond simple question-and-answer interaction by applying real counseling techniques such as expressions of empathy, restating the patient’s words in an organized way, and clarifying ambiguous content. This is intended to help patients talk about their condition more comfortably.
As a result of experiments conducted with 1,440 virtual patients to verify performance, the team confirmed that in most cases, the system effectively obtained key clinical information needed for treatment within just 30 minutes.
Based on the collected conversation, the AI generates a clinical dashboard that shows symptoms and potential conditions at a glance and provides it to medical staff. Through this, doctors can understand the patient’s condition more systematically before the patient enters the consultation room, allowing them to focus more on in-depth counseling with the patient during the actual consultation.
The core of this research is that AI is defined not as a replacement for doctors, but as a “coachable apprentice ” It is a collaborative model in which AI handles repetitive and structured information collection, while doctors make the final diagnosis and prescription based on that information.
The research team made clear that AI still has limitations in understanding subtle emotional changes or handling sensitive topics, and emphasized that final judgment must always be carried out by trained medical professionals.
Professor Uichin Lee stated, “If AI reduces the burden of the initial consultation stage, medical staff can focus more on deeper counseling with patients,” adding, “This shows the possibility of developing a new model of care in which humans and AI collaborate in medical settings.”
This study, with doctoral student Yugyeong Jung as the first author, was presented on April 13 at ACM CHI 2026 (ACM Conference on Human Factors in Computing Systems), the most prestigious conference in the field of human-computer interaction.
※ Paper title: “Toward Flexible Psychiatric History-Taking and Visualization: Exploring Clinician Perspectives with Large Language Models,” DOI: https://dl.acm.org/doi/10.1145/3772318.3790970 ※ Author information: Yugyeong Jung (KAIST, first author), Thu Hoang Anh Vo (KAIST, second author), Hyun Seung Moon (KAIST, third author), Jae Young Choi (KAIST, fourth author), Hyangkyeong Oh (Gangnam Severance Hospital, fifth author), Ujin Lee (Gangnam Severance Hospital, sixth author), Eunjoo Kim (Gangnam Severance Hospital, seventh author), Tak Yeon Lee (KAIST, corresponding author), Uichin Lee (KAIST, corresponding author)
This research was supported by the Digital Columbus Project of the Institute of Information & Communications Technology Planning & Evaluation (project title: Development of Digital Innovation Element Technologies for Predicting Complex Diseases in Advance and Expanding Non-Face-to-Face Care).
2026.05.26 View 381 -
“Why Only Copper?”… KAIST Reveals Key Limitation of Catalysts That Convert Carbon into Fuel
<(From left) Professor Jihun Oh, Ph.D candidate Suneon Wang, (Starting from the left circle) Dr. Beomil Kim, Ph.D candidate Seungchang Han, Professor Stefan Ringe>
Technology that converts carbon dioxide (CO₂) into fuels and plastic feedstocks using electricity is gaining attention as a core technology in the era of carbon neutrality. In particular, ethylene and ethanol are high-value materials widely used in the production of plastics, fuels, and chemical products, but until now, the only metal that has effectively produced them has essentially been copper (Cu). Through this study, Korean researchers have revealed the limitations of existing catalyst theories that have explained this principle.
KAIST (President Kwang Hyung Lee) announced on the 21st of May that a research team led by Professor Jihun Oh of the Department of Materials Science and Engineering, through joint research with Professor Stefan Ringe’s team from the Department of Chemistry at Korea University (President Dongwon Kim), has identified a new operating principle of the electrochemical CO₂ reduction reaction (CO₂ reduction reaction, a reaction that uses electricity to convert carbon dioxide into other chemical substances).
The research team fabricated alloy catalysts made by mixing gold (Au), silver (Ag), and palladium (Pd), and analyzed what substances these catalysts convert CO₂ into.
Existing catalyst theories have predicted that if the “d-band center” (an indicator of the electronic reactivity of a catalyst) and “work function” (the energy required for a metal to release electrons outward), which indicate the reactivity of electrons on the catalyst surface, are similar to those of copper, then the catalyst should be able to produce multi-carbon (C2+) compounds such as ethylene and ethanol like copper does.
Using a co-sputtering process (a technique that simultaneously deposits multiple metals as thin films to create a new alloy with a desired ratio), the research team precisely fabricated a ternary alloy (AuAgPd, an alloy made by mixing three metals: gold, silver, and palladium) with electronic properties very similar to those of copper.
However, the actual experimental results were different. This alloy produced simple products such as carbon monoxide (CO), but it did not produce complex multi-carbon compounds such as ethylene or ethanol at all. This means that complex CO₂ conversion reactions are difficult to explain using only the electronic properties of catalysts. In other words, the study confirmed that how atoms are arranged on the catalyst surface also has an important effect on reaction performance.
<Catalytic reactions that produce different products from the same carbon dioxide (AI image)>
The research team expects that this study will provide important clues for developing next-generation high-efficiency catalysts that can replace copper in the future. In particular, the study is significant in that it presents a new direction showing the need for precise catalyst design strategies that go beyond existing designs centered only on simple electronic structure and also consider atomic arrangement.
Professor Jihun Oh stated, “This study shows that existing catalyst theories alone are insufficient to fully explain complex multistep carbon conversion reactions,” adding, “In the future, a new catalyst design strategy that considers both electronic properties and local atomic arrangement, meaning how atoms are arranged on the catalyst surface, will be necessary.”
This paper, with KAIST Dr. Beomil Kim, doctoral student Suneon Wang, and Korea University Dr. Seungchang Han as first authors, was published in the May 2026 issue of the international journal Nature Catalysis.
※ Paper title: “Peaks and pitfalls of electrocatalytic CO₂ reduction descriptor models,” DOI: 10.1038/s41929-026-01526-7
※ Lead authors: Beomil Kim (KAIST, first author), Seungchang Han (Korea University, first author), Suneon Wang (KAIST, first author), Jihun Oh (KAIST, corresponding author), Stefan Ringe (Korea University, corresponding author)
This research was supported by the Nano and Material Technology Development Program, the Top-Tier Research Institution Collaboration Platform and Joint Research Support Program, and the Individual Research Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT, as well as by the National Supercomputing Center at the Korea Institute of Science and Technology Information (KISTI).
2026.05.21 View 472 -
Overcoming the Limits of Hydrogen Storage and Transport… KAIST Develops Next-Generation Ammonia Protonic Ceramic Fuel Cell
<(Top row, from left) Professor Kang Taek Lee, Professor Joongmyeon Bae, Dr. Tae Ho Shin, Dr. Ki-Min Roh, (Bottom row, from left) Dr. Dongyeon Kim, Researcher Dong Jae Park, Dr. Incheol Jeong>
As ammonia gains attention as a next-generation energy source capable of overcoming the limits of hydrogen storage and transport, KAIST and a joint research team have developed fuel cell technology that directly uses ammonia as fuel while achieving world-class performance and stability. This achievement is regarded as a core technology that will accelerate the commercialization of the next-generation hydrogen economy and carbon-free power generation.
KAIST (President Kwang Hyung Lee) announced on the 20th of May that Professor Kang Taek Lee and Professor Joongmyeon Bae of the Department of Mechanical Engineering, together with a joint research team including Dr. Tae Ho Shin of the Korea Institute of Ceramic Engineering and Technology (KICET, President Jong-Suk Yoon) and Dr. Ki-Min Roh of the Korea Institute of Geoscience and Mineral Resources (KIGAM, President Kwon Yi Kyun), have developed catalyst technology that dramatically improves the performance and durability of ammonia-based protonic ceramic fuel cells (PCFCs, next-generation high-efficiency fuel cells that generate electricity by transporting hydrogen ions).
<AI image: A next-generation fuel cell that generates electricity using ammonia (NH₃) as fuel>
Ammonia is attracting attention as a next-generation hydrogen carrier (Energy Carrier, a medium that stores and transports hydrogen) because it is easy to store and transport in liquid form. It is also regarded as a representative carbon-free fuel because it consists only of nitrogen (N) and hydrogen (H), producing almost no carbon dioxide (CO₂) during power generation. However, inside fuel cells, ammonia has caused problems by damaging nickel-based materials and slowing reaction rates, leading to performance degradation and shortened lifespan.
To solve this problem, the research team designed a new catalyst structure combining a “high-entropy” oxide catalyst (High-Entropy, a design method that enhances material stability and performance by mixing multiple elements) that improves structural stability by mixing multiple elements, with metal nanoparticles (Nano Particle, ultrafine metal particles on the nanometer scale) that form spontaneously on the surface during operation.
This catalyst was found not only to resist structural collapse even in an ammonia environment, but also to effectively promote the reaction that decomposes ammonia into hydrogen. Through density functional theory (DFT, Density Functional Theory, a simulation method that calculates reaction mechanisms at the atomic level) analysis, the research team identified that the high-entropy oxide structure lowers the energy barrier required for ammonia decomposition and promotes the formation of metal particles.
<AI-generated image of a high-entropy catalyst made by mixing multiple metallic elements>
In particular, the metal alloy nanoparticles that formed spontaneously on the catalyst surface showed much higher catalytic activity than single-metal catalysts. A fuel cell applying this catalyst recorded a maximum power density of 2.04 W per unit area (1 cm²) at 700°C. This means that high power can be produced from an area the size of a fingernail, representing world-class performance in the field of ammonia-based protonic ceramic fuel cells that generate electricity by transporting hydrogen ions (protons).
In addition, the cell operated stably for more than 255 hours even under harsh conditions of 600°C, significantly improving the problem of performance degradation (a phenomenon in which performance decreases over time) seen in existing catalysts.
<Schematic of an ammonia-fueled PCFC incorporating a high-entropy catalyst>
<Microstructure and elemental distribution results of the high-entropy catalyst>
Professor Kang Taek Lee stated, “Through the synergistic structure of high-entropy oxides and alloy nanoparticles, we improved both the performance and durability of ammonia fuel cells,” adding, “This study will serve as a catalyst for accelerating the commercialization of ammonia-based carbon-free power generation technology and next-generation hydrogen energy systems.”
This research, with Dr. Dongyeon Kim of the Department of Mechanical Engineering at KAIST, researcher Dong Jae Park of the Korea Institute of Ceramic Engineering and Technology, and Dr. Incheol Jeong of the Korea Institute of Geoscience and Mineral Resources as co-first authors, was published on April 17 in Nano-Micro Letters (IF: 36.3), an international journal in the fields of energy and materials.
※ Paper title: “Entropy-Modulated Oxide–Metal Catalyst Architectures for Direct Ammonia Protonic Ceramic Fuel Cells,” DOI: https://link.springer.com/article/10.1007/s40820-026-02194-9
This research was supported by the Mid-Career Researcher Program of the Ministry of Science and ICT, the Global Basic Research Laboratory Program, the InnoCORE Program of the Institutes of Science and Technology, and the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources.
2026.05.20 View 482 -
KAIST-Hanwha Solutions Establishes ‘Eco-Friendly Bio-Platform’ to Replace Petroleum-Derived Naphtha
<(From Left) Hyun Bae Bang, Cheon Woo Moon, Cindy Pricilia Surya Prabowo, Minjung Ki, Sang Yup Lee, Changhee Cho, (Upper Left) Jae Sung Cho, Namjin Jang>
KAIST announced on May 19th that the KAIST-Hanwha Solutions Future Technology Research Institute, has secured bio-technology capable of mass-producing eco-friendly raw materials for plastics and textiles using waste resources, offering an alternative to petroleum-derived naphtha.
Naphtha, an essential feedstock for the petrochemical industry, has faced sharp price increases and supply instability in recent years, driving demand for sustainable alternatives. The new technology, addresses both resource supply stability and environmental concerns simultaneously.
A study led by Distinguished Professor Sang-yup Lee of the Department of Chemical and Biomolecular Engineering was published on May 12th in the journal Nature Chemical Engineering and has been selected as the cover paper for the May issue, a designation reserved for research achievements that represent the corresponding issue.
This platform uses ‘glycerol,’ a byproduct discarded during the biodiesel production process, as a raw material. The team engineered high-efficiency microorganisms to convert this waste into 1,3-propanediol (1,3-PDO), a key material for plastics and cosmetics, and optimized the fermentation process for industrial application. The research team succeeded in maintaining high production level even in a 300L pilot process, which serves as a test production stage before application in large-scale plant facilities, moving beyond the laboratory scale.
This study also used computer simulations to predict which genes to engineer, which resulted in improved production levels. The team also developed the fermentation system without antibiotic supplementation — a significant advance, as antibiotic use in industrial fermentation raises concerns about antimicrobial resistance and regulatory hurdles for food, cosmetic, and pharmaceutical applications.
< (AI Image) Microbial-based process for 1,3-propanediol (1,3-PDO) production >
The achievement reflects a 10-year partnership between KAIST and Hanwha Solutions that began in November 2015, with researchers from both sides working together directly on the experiments. Through the KAIST-Hanwha Solutions Future Technology Research Institute, the collaboration has produced 6 patent applications and 13 published papers, standing as a representative model of industry-academic cooperation in South Korea.
< Schematic diagram of microbial-based metabolic engineering strategies for 1,3-PDO production >
※ Paper Title: High-titer, antibiotic-free, pilot-scale production of 1,3-propanediol by engineered Corynebacterium, DOI: 10.1038/s44286-026-00389-w
※ Authors: Jae Sung Cho (KAIST, First Author), Cindy Pricilia Surya Prabowo (KAIST, First Author), Taehee Han (KAIST), Cheon Woo Moon (KAIST), Yoo-Sung Ko (KAIST), Changhee Cho (Hanwha Solutions), Je Woong Kim (KAIST), Won Jun Kim (Hanwha Solutions), Hyun Bae Bang (Hanwha Solutions), Jae Eun Lee (KAIST), Minjung Ki (KAIST), Namjin Jang (Hanwha Solutions), Sang Yup Lee (KAIST, Corresponding Author)
Jung-dae Kim, Head of the Research Institute at Hanwha Solutions, said, “This research is highly significant in that it confirmed the possibility of replacing existing petrochemical processes using bio-based raw materials. We expect it to be an important foundation for sustainable chemical material production and industrial application in the future.”
KAIST Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering stated, “This research is a case showing that microorganism-based chemical production can be sufficiently expanded to an actual industrial scale beyond the laboratory. It will contribute to producing various chemical materials in a more eco-friendly way in the future.”
2026.05.19 View 924 -
KAIST Study Identifies the Gentrification Paradox of Climate Adaptation
< (From left) Ph.D candidate Hyemin Cho, Professor Seung Kyum Kim >
'Green-Blue Adaptation (Climate adaptation based on green and water spaces),' which utilizes green and water spaces such as creating urban parks and restoring wetlands, is considered a representative climate adaptation strategy to reduce flood and heatwave damage in cities in the era of climate crisis. However, A KAIST-led international research team has demonstrated for the first time with continent-scale data that such climate adaptation policies can paradoxically stimulate housing price increases and population influx, thereby worsening the housing instability of existing low-income residents.
KAIST announced on May 18th that a research team led by Professor Seung Kyum Kim of the Department of Department of AI and Futures Studies, along with researchers from Peking University and New York University Shanghai, analyzed cities in 32 African countries and empirically identified the 'Gentrification Paradox (a phenomenon where environmental improvement paradoxically leads to the displacement of existing residents ),' showing that climate adaptation policies can increase urban resilience while simultaneously triggering social exclusion pressure.
The research team tracked changes from 2005 to 2024 targeting 5,503 administrative units within 221 urban areas in 32 African countries. By combining satellite image analysis with socio-economic data, they precisely analyzed the impacts that green-blue adaptation has had on actual cities and residents' lives.
In particular, the research team focused on the fact that climate adaptation policies affect not only environmental improvement effects but also socio-economic changes such as housing price increases and demographic shifts .
To this end, they applied the Difference-in-Differences method (a statistical technique that verifies policy effects by comparing changes before and after policy implementation), which analyzes the causal relationship of policy effects, to verify the impact of green-blue adaptation on urban changes. This study is highly significant as it is the first continent-scale analysis to identify the causal relationship between climate adaptation and gentrification across Africa.
Areas with green–blue adaptation interventions showed an approximately 41% increase in the Composite Gentrification Index. Housing prices rose by about 13%, household consumption increased by 20.3%, and population inflows also increased significantly.. This shows that facilities established to protect citizens from the climate crisis can paradoxically act as a factor that deepens the housing instability of economically vulnerable groups and weakens existing communities.
< Distribution of the start timing of Green-Blue Adaptation interventions by administrative unit >
This study suggests that climate adaptation should be viewed not simply as a matter of infrastructure construction, but as a 'distribution problem' of how to allocate benefits and burdens. The research team proposed that future climate policies should not stop at expanding green and blue infrastructure , but should be pursued alongside housing stabilization measures such as protecting land ownership rights, supplying public housing, and capturing development gains
Professor Seung Kyum Kim said, "Climate adaptation can make cities safer, but it can also increase housing prices and trigger demographic shifts that heighten housing insecurity for existing residents,” said Professor Seung Kyum Kim. “Future climate policies must consider not only environmental improvement, but also the protection of vulnerable groups, housing stability, and land tenure security."
In this study, PhD student Hyemin Cho from the KAIST Graduate School of Green Growth and Sustainability, Professor Longfeng Wu from Peking University, and Professor ChengHe Guan from New York University Shanghai participated, and the research results were published in the international academic journal 'Nature Cities' on April 13.
※ Article Title: The gentrification paradox of green-blue adaptation in African cities, https://www.nature.com/articles/s44284-026-00432-0 ※ Author Information: Seung Kyum Kim (KAIST, First Author & Corresponding Author), Hyemin Cho (KAIST, Second Author), Longfeng Wu (Peking University, Third Author), ChengHe Guan (NYU Shanghai, Co-Corresponding Author)
This study was conducted with the support of the 'Development of Next-Generation Integrated Evaluation Model for AI-Based Climate-Human Interaction' project under the AI-Based Future Climate Technology Development Source Research Program supported by the Ministry of Science and ICT.
2026.05.18 View 445 -
Why Do Dementia and Cognitive Decline Patients Remain Stuck in Past Memories?… KAIST Identifies Memory-Switching Mechanism
<(From Left) Professor Jin-Hee Han, Dr. Mujun Kim>
“Why do patients with dementia or cognitive decline remain stuck in past memories?”
KAIST researchers have identified, for the first time in the world, the existence of a “neural switch” in the brain that selectively retrieves the most recent memories. This study reveals the principle by which the brain selects necessary information between past memories and new memories, presenting new possibilities for future treatments for memory decline and reduced cognitive flexibility.
KAIST announced on the 17th of May that a research team led by Professor Jin-Hee Han of the Department of Biological Sciences has discovered, for the first time in the world, that a specific neural circuit connecting the medial septum (MS, a brain region that regulates memory and learning) and the medial entorhinal cortex (MEC, a brain region connected to the hippocampus* that processes memory information) switches between past and recent memories and plays a key role in selecting up-to-date information appropriate for the situation.
*Hippocampus: a key brain region that creates and stores new memories
We live by updating our memories through new experiences every day. For example, if the restaurant we visited today was more satisfying than the one we visited yesterday, the brain modifies the existing memory to reflect the new information. In this way, the ability to select necessary information between past and present memories is central to higher cognitive functions such as decision-making, problem-solving, future prediction, and logical reasoning. However, the principle by which the brain distinguishes and switches between memories has long remained unknown.
The research team focused on the medial septum, located deep within the brain. The medial septum regulates the activity rhythms of the hippocampus and acts as a “conductor” that helps the brain effectively store and retrieve information.
The study found that when specific neurons in the medial septum send signals to the medial entorhinal cortex, a brain region that processes memory information and delivers it to the hippocampus, the brain is better able to recall recent memories.
<(AI image) An inhibitory neural circuit switch in the MS–MEC pathway that regulates the selection between past and recent memories>
Conversely, when the research team artificially blocked this neural circuit using light, experimental animals were unable to use recent information and behaved according to past patterns. Neural activity in the hippocampus, which plays an important role in memory representation, also reverted to a past state. This shows that the circuit acts as a “neural switch” that selects the most recent information needed for the current situation among multiple memories.
The research team also analyzed memory performance according to brain activity states. Our brain repeatedly shifts between an “online state” (theta waves, brain waves activated during learning and concentration), in which it actively processes information, and an “offline state” (delta waves, slow brain waves that appear during sleep or rest), which is a resting state.
The analysis showed that the longer the online state was maintained, the better recent memories were recalled, while frequent switching between online and offline states significantly reduced memory retrieval ability. This suggests that specific brain rhythms and states are important neurobiological indicators that determine effective memory retrieval.
This study is significant in that it identified the mechanism by which the brain flexibly reflects new information while maintaining past memories. The research team expects that this discovery could lead to the development of new therapeutic technologies to improve memory decline and reduced cognitive flexibility in patients with degenerative brain diseases such as dementia and Alzheimer’s disease.
<An inhibitory neural circuit switch in the MS–MEC pathway that regulates the selection between past and recent memories>
Professor Jin-Hee Han stated, “This study presents a new paradigm for understanding the principle by which our brain organizes and uses numerous experiences in chronological order,” adding, “Previously, memory retrieval was understood simply as the replaying of stored traces, but through this study, we proved that the brain has a regulatory system that actively selects recent information among competing memories.”
This study involved Dr. Mujun Kim of the Department of Biological Sciences at KAIST, doctoral students Boin Suh, Sunhoi So, Jung Wook Choi, Jaemin Hwang, and Juhee Park, and was published on April 29 in Nature Neuroscience, a top-tier international journal in neuroscience.
※ Paper title: “A septo-entorhinal GABAergic pathway that enables switching between episodic memories,” https://doi.org/10.1038/s41593-026-02280-6 ※ Author information: Mujun Kim (KAIST, first author), Boin Suh (KAIST), Sunhoi So (KAIST), Jung Wook Choi (KAIST), Jaemin Hwang (KAIST), Juhee Park (KAIST) & Jin-Hee Han (KAIST, corresponding author)
This research was supported by the Mid-Career Research Program (National Research Foundation of Korea), the Samsung Science and Technology Foundation, and the KAIST Jang Young Sil Fellow Program.
2026.05.18 View 723 -
KAIST Wins Best Paper Award at Top HCI Conference for Research on Games Played by Plants
<(From Left) Professor Chang Hee Lee, Ph.D candidate Yoonji Lee>
A new type of digital game has emerged in which plants themselves change characters in the game, while humans observe and emotionally engage with them.
KAIST announced on the 15th of May that a research team led by Professor Chang Hee Lee of the Department of Industrial Design won the Best Paper Award at ACM CHI 2026, the most prestigious conference in the field of Human-Computer Interaction (HCI), for research that uses plants not as simple decorations or sensors but as “agents of interaction.”
< Plant.play system image >
< Plant.play system side view photo >
ACM (Association for Computing Machinery) CHI (Conference on Human Factors in Computing Systems) 2026 was held from April 13 to 17 in Barcelona, Spain. CHI is one of the world’s most prestigious international conferences in the field of Human-Computer Interaction (HCI).
The Best Paper Award is the highest honor awarded to only about the top 1% of all submitted papers. In particular, this year’s conference received a total of 6,730 paper submissions, marking the largest scale in its history, and this award is regarded as an achievement demonstrating the global research competitiveness of KAIST researchers.
Professor Chang Hee Lee’s team proposed a new form of interaction in which plants directly participate in digital games through the paper “When Plants Play: Rethinking Plant Materiality in Digital Games.”
This research is characterized by a design that goes beyond the conventional approach of using plants simply as sensors or decorative elements, allowing changes in the plant’s state to directly affect the progress of the game. The research team reflected the plant’s bioelectrical signals, environmental data, and circadian rhythms (biological changes that repeat according to day and night) in the game, enabling the character in the game to change according to the plant’s state. Rather than directly controlling the game, users participate by observing and interpreting the plant’s changes and responses.
As the plant grows, it creates different forms of characters and changes, and these changes reflect the plant’s own growth patterns and pace of transformation.
As a result of conducting user research in an actual exhibition environment, the research team confirmed that participants accepted the plant’s slow and unpredictable changes as a form of “play.” In particular, participants also showed a tendency to become emotionally immersed in and empathize with both the plant and the virtual character in the game. This led them to perceive the plant not simply as an object of observation, but as an entity with which they interact.
<A digital pet raised by the plant shows various behaviors—such as reading books—according to its daily rhythm, and grows over time>
<In a low-humidity environment, the plant provides hamburgers to the digital pet to help it grow>
This research received high recognition for moving beyond human-centered digital interaction and proposing new possibilities for interaction with nonhuman entities such as plants.
Professor Chang Hee Lee stated, “This research is an attempt to view nonhuman entities such as plants as agents and explore new forms of interaction,” adding, “Our society is expanding into an ‘attachment economy,’ which values emotional bonds and empathy, and in the future, emotional engagement not only with humans but also with diverse nonhuman entities such as AI, robots, animals, and plants will become important.” He continued, “This research is an example that demonstrates these new possibilities for interaction.”
This study, with doctoral student Yoonji Lee as the first author and Professor Chang Hee Lee as the corresponding author, can be found in the ACM Digital Library.
※ Paper title: “When Plants Play: Rethinking Plant Materiality in Digital Games” ※ DOI: https://doi.org/10.1145/3772318.3791373
This research was supported by Brain Korea (BK21).
2026.05.18 View 507 -
KAIST Develops Real-Time Diagnostic Smart Dressing Patch to End the Fear of Diabetic Foot Amputation
<(From Left) Professor Inkyu Park, Dr. Seokjoo Cho, (Upper Right, From Left) Professor Ji-Hwan Ha, Researcher Jun-Ho Jeong , Professor Wei Gao>
“Diabetic ulcers,” which occur in patients with diabetes, are dangerous complications that can lead to amputation if the treatment window is missed. A joint research team has developed a “smart dressing patch” that can monitor wound conditions in real time.
KAIST (President Kwang Hyung Lee) announced on the 14th of May that a research team led by Distinguished Professor Inkyu Park of the Department of Mechanical Engineering, through joint research with Professor Ji-Hwan Ha of Hanbat National University (President Yongjun Oh), researcher Jun-Ho Jeong of the Korea Institute of Machinery & Materials (President Seog-Hyeon Ryu), and Professor Wei Gao of the California Institute of Technology (Caltech; President Thomas F. Rosenbaum) in the United States, has developed a “wireless, battery-free optoelectronic multi-modal sensor patch” for diabetic ulcer management.
The patch developed by the research team combines an optoelectronic sensor, which can simultaneously measure multiple types of biological information, with a functional dressing. It can analyze glucose concentration, acidity (pH, an indicator of hydrogen ion concentration), and temperature changes at the wound site in real time, and patients can check their condition themselves using a smartphone.
The research team fabricated a functional nanofiber dressing using electrospinning, a method that uses an electric field to create fibers much thinner than a human hair. This dressing changes color in response to increased glucose and changes in acidity that appear in diabetic foot wounds. In other words, if the wound condition worsens, the dressing color changes, allowing danger signals to be easily checked with the naked eye. Through this, abnormal signs that could lead to tissue necrosis can be detected and tracked over long periods in a non-invasive manner, meaning without cutting the skin or drawing blood.
The research team combined this with an optoelectronic system to improve diagnostic accuracy. A light-emitting diode (LED, a semiconductor device that converts electricity into light) embedded in the patch and a photodiode, a semiconductor sensor that detects light, measure the color change of the dressing as light reflectance and then convert it into an electrical signal.
This provides more accurate and stable data than ordinary camera-based imaging because it is less affected by changes in surrounding lighting.
In particular, the patch operates without a separate battery by applying a flexible circuit based on near field communication (NFC), a wireless communication technology that exchanges data over short distances. When a smartphone is placed near the sensor, the patch receives power wirelessly and operates, transmitting the measured data in real time. In other words, patients and medical staff can immediately check and respond to wound conditions using only a smartphone app, without separate complex equipment.
< Conceptual Diagram of a Multimodal Colorimetric Dressing and Optoelectronic Sensor for Diagnosing Diabetic Foot and Diabetic Diseases >
The technology developed in this study has high clinical value because it provides both intuitive visual signals and quantitative electronic data while imposing no physical burden on patients. It is also expected to contribute to improving the quality of life of patients with diabetes by enabling continuous wound management without repeated blood sampling.
Distinguished Professor Inkyu Park stated, “Research that began to reduce the pain of diabetic patients who have to prick their fingers with a needle every day has led to a technology for the preemptive diagnosis of complications,” adding, “This technology will become a core platform technology that can be expanded in the future to blood-free diagnostic technologies not only for diabetes but also for various chronic diseases.”
In this study, KAIST Dr. Seokjoo Cho and Professor Ji-Hwan Ha of Hanbat National University participated as co-first authors. The research results were published on March 26, 2026, in the international materials science journal Advanced Functional Materials. The paper was also selected as a Front Cover article of the journal.
※ Paper title: “Wireless, Battery-Free, Optoelectronic, Multi-Modal Sensor Integrated With Colorimetric Dressing for Diabetic Ulcer Management,” DOI: 10.1002/adfm.202532167
< Front Cover Image >
This research was supported by the Mid-Career Researcher Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT, the Alchemist Project of the Ministry of Trade, Industry and Energy, and the Daejeon RISE Center.
2026.05.14 View 964