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KAIST-KakaoBank Speeds Up 'Explainable AI' by 11 Times: "Boosts Financial AI Reliability
< (From left) Professor Jaesik Choi of the Kim Jaechul Graduate School of AI, Ph.D candidate Chanwoo Lee, Ph.D candidate Youngjin Park >
The research team led by Professor Jaesik Choi of KAIST's Kim Jaechul Graduate School of AI, in collaboration with KakaoBank Corp, announced that they have developed an accelerated explanation technology that can explain the basis of an Artificial Intelligence (AI) model's judgment in real-time. This research achievement significantly increases the practical applicability of Explainable Artificial Intelligence (hereinafter XAI) technology in fields requiring real-time decision-making, such as financial services, by achieving an average processing speed 8.5 times faster, and up to 11 times faster, than existing explanation algorithms for AI model predictions.
In the financial sector, a clear explanation for decisions made by AI systems is essential. Especially in services directly related to customer rights, such as loan screening and anomaly detection, regulatory demands to transparently present the basis for the AI model's judgment are increasingly stringent. However, conventional Explainable Artificial Intelligence (XAI) technologies required the repeated calculation of hundreds to thousands of baselines to generate accurate explanations, resulting in massive computational costs. This was a major factor limiting the application of XAI technology in real-time service environments.
To address this issue, Professor Choi's research team developed the 'ABSQR (Amortized Baseline Selection via Rank-Revealing QR)' framework for accelerating explanation algorithms. ABSQR noticed that the value function matrix generated during the AI model explanation process has a low-rank structure. It introduced a method to select only a critical few baselines from the hundreds available. This drastically reduced the computation complexity, which was previously proportional to the number of baselines, to be proportional only to the number of selected critical baselines, thereby maximizing computational efficiency while maintaining explanatory accuracy.
Specifically, ABSQR operates in two stages. The first stage systematically selects important baselines using Singular Value Decomposition (SVD) and Rank-Revealing QR decomposition techniques. Unlike existing random sampling methods, this is a deterministic selection method aimed at preserving information recovery, which guarantees the accuracy of the explanation while significantly reducing computation. The second stage introduces an amortized inference mechanism, which reuses the pre-calculated weights of the baselines through cluster-based search, allowing the system to provide an explanation for the model's prediction result in real-time service environments without repeatedly evaluating the model. The research team verified the superiority of ABSQR through experiments on various real-world datasets. Tests on standard datasets across five sectors—finance, marketing, and demographics—showed that ABSQR achieved an average processing speed 8.5 times faster than existing explanation algorithms that use all baselines, with a maximum speed improvement of over 11 times. Furthermore, the degradation of explanatory accuracy due to speed acceleration was minimized, maintaining up to 93.5% of the explanation accuracy compared to the baseline algorithm. This level is sufficient to meet the explanation quality required in real-world applications.
< ABSQR Framework Overview. (1) The baseline selection stage utilizes the low-rank structure of the value function matrix to select only a small number of key baselines, and (2) the accelerated search stage reuses the pre-calculated baseline weight coefficients based on clusters. This dramatically reduces the computation complexity, which was proportional to the number of baselines, to be proportional only to the number of selected key baselines. >
A KakaoBank official stated, "We will continue relentless research and development to enhance the reliability and convenience of financial services and introduce innovative financial technologies that customers can experience." Chanwoo Lee and Youngjin Park, co-first authors from KAIST, explained the significance of the research: "This methodology solves the crucial acceleration problem for real-time application in the financial sector, proving that it is possible to provide users with the reasons behind a learning model's decision in real-time." They added, "This research provides new insights into what constitutes unnecessary computation and the selection of important baselines in explanation algorithms, practically contributing to the improvement of explanation technology efficiency." This research, co-authored by PhD candidates Chanwoo Lee and Youngjin Park from the KAIST Kim Jaechul Graduate School of AI, and researchers Hyeongeun Lee and Yeeun Yoo from the KakaoBank Financial Technology Research Institute, was presented on November 12 at the 'CIKM 2025 (ACM International Conference on Information and Knowledge Management)', the world's highest-authority academic conference in the field of information and knowledge management. ※ Paper Title: Amortized Baseline Selection via Rank-Revealing QR for Efficient Model Explanation
※ Author Information:
※ Author Information: DOI: https://doi.org/10.1145/3746252.3761036
Co-First Authors: Chanwoo Lee (KAIST Kim Jaechul Graduate School of AI), Youngjin Park (KAIST Kim Jaechul Graduate School of AI), Hyeogeun Lee (KakaoBank), Yeeun Yoo (KakaoBank)
Co-Authors: Daehee Han (KakaoBank), Junho Choi (KAIST Kim Jaechul Graduate School of AI), Kunhyung Kim (KAIST Kim Jaechul Graduate School of AI)
Corresponding Authors: Nari Kim (KAIST Kim Jaechul Graduate School of AI), Jaesik Choi (KAIST Kim Jaechul Graduate School of AI)
Meanwhile, this research achievement was conducted through KakaoBank's industry-academia research project 'Advanced Research on Explainable Artificial Intelligence Algorithms in the Financial Sector' and the Ministry of Science and ICT/Institute for Information & Communications Technology Planning and Evaluation (IITP) supported project 'Development of Explainable Artificial Intelligence Technology Providing Explainability in a Plug-and-Play Manner and Verification of Explanation Provision for AI Systems.'
2025.12.12 View 2592 -
Octopus-Inspired 3D Micro-LEDs Pave the Way for Selective Pancreatic Cancer Therapy
<(From Left) Professor Keon Jae Lee, Professor Tae-Hyuk Kwon, Ph.D candidate Min Seo Kim, Dr. Jae Hee Lee, Dr. Chae Gyu Lee>
-KAIST and UNIST Researchers Develop Shape-Morphing Device to Overcome Pancreatic Tumor Microenvironment Barriers
Conventional pancreatic cancer treatments face a critical hurdle due to the dense tumor microenvironment (TME). This biological barrier surrounds the tumor, severely limiting the infiltration of chemotherapy agents and immune cells. While photodynamic therapy (PDT) offers a promising alternative, existing external light sources, such as lasers, fail to penetrate deep tissues effectively and pose risks of thermal damage and inflammation to healthy organs
To address these challenges, Professor Keon Jae Lee’s team at KAIST, in collaboration with Professor Tae-Hyuk Kwon at UNIST, developed an implantable, shape-morphing 3D micro-LED device capable of effectively delivering light to deep tissues. The key technology lies in the device’s flexible, octopus-like architecture, which allows it to wrap around the entire pancreatic tumor. This mechanical compliance ensures uniform light delivery to the tumor despite the tumor’s physiological expansion or contraction, enabling continuous, low intensity photostimulation that precisely targets cancer cells while preserving normal tissue.
In in-vivo experiments involving mouse models, the device demonstrated remarkable therapeutic efficacy. Within just three days, tumor fibrous tissue was reduced by 64%, and the pancreatic tissue successfully reverted to normal tissue, overcoming the limitations of conventional PDT.
Prof. Keon Jae Lee said, "This research presents a new therapeutic paradigm by directly disrupting the tumor microenvironment, the primary obstacle in pancreatic cancer treatment." He added, "We aim to expand this technology into a smart platform integrated with artificial intelligence (AI) for real-time tumor monitoring and personalized treatment. We are currently seeking partners to advance clinical trials and commercialization for human application."
<Overall concept of 3D Shape-morphing micro-LEDs (SMLEDs). The 3D long-term, low-intensity photodynamic therapy (PDT) system attaches to the pancreatic surface, ensuring stable and continuous light delivery. Initially maintaining a 2D structure, the system morphs into a 3D structure upon implantation to conform to the shape of the pancreas. In in vivo experiments, the device maintained stable adhesion without detachment for four weeks and reduced the pancreatic tumor size by 64%.>
Professor Tae-Hyuk Kwon commented, "While phototherapy is effective for selective cancer treatment, conventional technologies have been limited by the challenges of delivering light to deep tissues and developing suitable photosensitizers." He added, "Building on this breakthrough, we aim to expand effective immune-based therapeutic strategies for targeting intractable cancers."
<Cover Image. The 3D long-term, low-intensity photodynamic therapy (PDT) system, developed by Professor Keon Jae Lee's team at the Department of Materials Science and Engineering at KAIST, was featured as the cover article of the international journal Advanced Materials>
The result, titled "Deeply Implantable, Shape-Morphing, 3D MicroLEDs for Pancreatic Cancer Therapy," was featured as the cover article in Advanced Materials (Volume 37) on December 10, 2025.
2025.12.11 View 2502 -
Robot Valley Project Activation of the Korean style Robot and AI Startup Ecosystem Fully Underway
< From left: Top Excellence Award winner Robolight (Pre-startup Founder Han-seol Choi), Top Excellence Award winner Coils (CEO Seong-ryeol Heo), Professor Jung Kim of KAIST, Grand Prize winner Noman (CEO Jung-wook Moon), Professor Kyoungchul Kong of KAIST, CEO Dae-hee Park of Daejeon Creative Economy Innovation Center, Excellence Award winner Gigaflops (CEO Min-tae Kim), Excellence Award winner BLUE APEX (Pre-startup Founder Na-hyeon Kwon) >
KAIST announced on December 10th that KAIST Holdings (CEO Hyeonmin Bae), a specialized technology commercialization investment institution, successfully held the '2025 KAIST Hu-Robotics Startup Cup' on the 9th at the main building of Daejeon Startup Park. This was held as part of the Robot Valley Project, aiming to discover and foster promising startup teams in the robotics field and establish a robot scale-up ecosystem based on a technology platform.
This competition was conducted as a core program of the Robot Valley Project (Deep-Tech Scale-up Valley Fostering Project), which is promoted by the Ministry of Science and ICT and supported by Daejeon Metropolitan City. The competition proceeded through a meet-up day with KAIST Mechanical Engineering researchers, robotics companies like Angel Robotics and Twinny, and startup experts such as Bluepoint, leading to the final round. Throughout this process, a support system for the scale-up of robot startups was established, linking technology verification, strengthening entrepreneurial capabilities, and investment linkage.
KAIST Holdings and the Deep-Tech Valley Project Group (hereinafter referred to as the Project Group) stated that this competition marks the beginning of 'establishing a Korean-style Robot and AI startup ecosystem.' Their goal through the Robot Valley Project is to create a Korean-style robot scale-up ecosystem centered around Daejeon and KAIST, and furthermore, to build a technology circulation structure utilizing verified technology platforms.
KAIST has produced successful scale-up cases in the robotics field, such as Rainbow Robotics and Angel Robotics. However, the recent robotics industry has seen a rapid increase in technological difficulty due to the convergence of mechanical engineering, AI, and control software, creating structural limitations for early-stage founders to challenge alone.
To solve this, the Project Group proposed the 'Scale-up Valley Construction Strategy,' which opens up the verified technologies of established senior companies to junior founders. This strategy focuses on supporting startups to concentrate on developing market-ready robot services and applications on top of verified technology platforms, rather than consuming excessive time on developing basic hardware like motors and controllers.
The Angel Robotics technology platform, presented as the core underlying technology of this strategy, consists of actuators, control modules, and core software. KAIST plans to gradually open up these foundational technologies for use by early-stage startup teams.
The Project Group emphasized that enabling startup teams to utilize such technology platforms from the initial stage is the core infrastructure for accelerating the Korean-style robot startup ecosystem.
A total of 21 teams participated in this competition, including pre-startup founders (Track A) and early-stage startups established within 3 years (Track B), all possessing human-centered robotics technology and convergence business models.
After fierce preliminaries, 8 teams advanced to the final round, and a total of 5 teams were finally selected: one Grand Prize winner, two Choi Woo-sung (Top Excellence Award) winners, and two Excellence Award winners.
The Grand Prize was awarded to 'Noman' for proposing an integrated system for a strawberry farm work robot and a rotating vertical cultivation module.
The Woo-sung Choi (Top Excellence Award) went to 'Robolight' and 'Coils.'
The Excellence Award was awarded to BLUE APEX and Gigaflops.
Professor Jung Kim, Head of the KAIST Mechanical Engineering Department and General Manager of the Robot Valley Project, said, "This competition has become the starting point for discovering future robot unicorns. For the next three years, we will continue to provide practical support for the growth of robot startups, and KAIST will play a leading role in building and expanding the deep-tech robot ecosystem centered in Daejeon."
< Group Photo of Award Winners >
Meanwhile, this competition was jointly hosted and organized by the Ministry of Science and ICT, Daejeon Metropolitan City, and the Research and Business Development Special Zone Foundation, as well as startup support organizations including KAIST, KAIST Holdings, Daejeon Technopark, and Daejeon Creative Economy Innovation Center.
2025.12.10 View 2906 -
KAIST Predicts Human Group Behavior with AI! 1st Place at the World’s Top Conference… Major Success after 23 Years
<(From Left) Ph.D candidate Geon Lee, Ph.D candidate Minyoung Choe, M.S candidate Jaewan Chun, Professor Kijung Shin, M.S candidate Seokbum Yoon>
KAIST (President Kwang Hyung Lee) announced on the 9th of December that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed a groundbreaking AI technology that predicts complex social group behavior by analyzing how individual attributes such as age and role influence group relationships.
With this technology, the research team achieved the remarkable feat of winning the Best Paper Award at the world-renowned data mining conference “IEEE ICDM,” hosted by the Institute of Electrical and Electronics Engineers (IEEE). This is the highest honor awarded to only one paper out of 785 submissions worldwide, and marks the first time in 23 years that a Korean university research team has received this award, once again demonstrating KAIST’s technological leadership on the global research stage.
Today, group interactions involving many participants at the same time—such as online communities, research collaborations, and group chats—are rapidly increasing across society. However, there has been a lack of technology that can precisely explain both how such group behavior is structured and how individual characteristics influence it at the same time.
To overcome this limitation, Professor Kijung Shin’s research team developed an AI model called “NoAH (Node Attribute-based Hypergraph Generator),” which realistically reproduces the interplay between individual attributes and group structure.
NoAH is an artificial intelligence that explains and imitates what kinds of group behaviors emerge when people’s characteristics come together. For example, it can analyze and faithfully reproduce how information such as a person’s interests and roles actually combine to form group behavior.
As such, NoAH is an AI that generates “realistic group behavior” by simultaneously reflecting human traits and relationships. It was shown to reproduce various real-world group behaviors—such as product purchase combinations in e-commerce, the spread of online discussions, and co-authorship networks among researchers—far more realistically than existing models.
< The process of generating group interactions using NoAH >
Professor Kijung Shin stated, “This study opens a new AI paradigm that enables a richer understanding of complex interactions by considering not only the structure of groups but also individual attributes together,” and added, “Analyses of online communities, messengers, and social networks will become far more precise.”
This research was conducted by a team consisting of Professor Kijung Shin and KAIST Kim Jaechul Graduate School of AI students: master’s students Jaewan Chun and Seokbum Yoon, and doctoral students Minyoung Choe and Geon Lee, and was presented at IEEE ICDM on November 18.
※ Paper title: “Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes” Original paper: https://arxiv.org/abs/2509.21838
< Photo from the award ceremony held on November 14 at the International Spy Museum in Washington, D.C.>
Meanwhile, including this award-winning paper, Professor Shin’s research team presented a total of four papers at IEEE ICDM this year. In addition, in 2023, the team also received the Best Student Paper Runner-up (4th place) at the same conference.
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-202400457882, AI Research Hub Project) (RS-2019-II190075, Artificial Intelligence Graduate School Program (KAIST)) (No. RS-2022-II220871, Development of AI Autonomy and Knowledge Enhancement for AI Agent Collaboration).
2025.12.09 View 3054 -
KAIST Drives National Competitiveness with a Dual-Impact Model for AI Research and Regional Innovation
<Photo of KAIST Students>
KAIST announced on December 9th that it will accelerate the nurturing of world-class scientific talent and regional balanced development. This follows the government's recent announcement on 'Leaping to a Science and Technology Powerhouse, the Republic of Korea, Where People Dream of Becoming Science and Technology Professionals Again (Nov. 7),' which explicitly named the four major science and technology institutes, including KAIST, as AX (AI Transformation) innovation hubs and key leading institutions for regional innovation.
This move aligns with the policy direction of President Jae-myung Lee. On November 4th, President Jae-myung Lee stated in a Cabinet meeting, "STEM talent is the core of national competitiveness," adding that "the increase in applicants for early admissions to the four major science and technology institutes is a very desirable phenomenon for the nation's future." In particular, the President requested that the government "actively seek concrete policies, such as expanding the allowance for transfers between STEM fields, increasing budget support, securing excellent faculty, and upgrading research and education infrastructure, because science and technology institutes can also significantly contribute to regional balanced development."
KAIST President Kwang Hyung Lee stated, "Strengthening AI research capabilities and regional balanced development is a Dual-Impact Model for AI Research and Regional Innovation that boosts national competitiveness." He confirmed that through the government's policy direction, the innovation philosophy KAIST has pursued—that 'the region is national competitiveness'—has been established as a core national direction.
In reality, KAIST continues to firmly play a central role in nurturing the talent that sustains South Korea's science and technology sector, even amid the deepening phenomenon of students flocking to medical schools. The increase in early admission applicants to the four science and technology institutes proves the successful establishment of education and research foundations where students can choose the dream of becoming science and technology professionals instead of doctors. To accelerate this trend, KAIST is focusing on establishing a National AI Research Lab and pioneering the next-generation AI research paradigm with the goal of becoming one of the top three AI powerhouses (G3) globally.
Our university was selected not only to lead the development of the next-generation bio-AI model 'K-Fold'—which surpasses Google DeepMind—and as a key participating institution in the Lunit consortium, but also as a core research team in the national AI flagship project, the 'Generative AI Leading Talent Cultivation Program.' Through discovering research topics that reflect diverse technological demands from industries, nurturing advanced AI talent, and demonstrating research outcomes in industrial settings, KAIST is being reborn as a field-ready leader guiding the AI Transformation (AX) across all of South Korea's industries.
KAIST's AI research competitiveness has also been officially recognized overseas. NVIDIA CEO Jensen Huang personally introduced KAIST as an "Amazing University" during his keynote speech at the 2025 APEC CEO Summit (Oct. 31), highly evaluating KAIST's world-class research capabilities and global collaboration potential.
Regional innovation is also gaining momentum. Our university is expanding physical AI-based research infrastructure in regions like Jeonbuk and Gyeongnam, centered around its main campus in Daejeon. Through the AI and robot-based 'Robot Valley Project' and the 'Global Innovation Startup Growth Hub Project,' in cooperation with Daejeon City, KAIST is supporting the advancement of local industries and the growth and global expansion of startups.
<ANGEL SUIT, a gait-training robot>
In particular, Sovagen—a bio-company founded on the technology of Professor Jeong Ho Lee of the KAIST Graduate School of Medical Science—recently succeeded in an overseas technology transfer of an RNA new drug for epilepsy valued at 750 billion KRW, proving a virtuous cycle model of innovation where university research translates into actual industry success.
Furthermore, the foundation for future talent development is being strengthened through efforts like promoting a culture of challenging research via the 'Failure Lab,' and early nurturing of outstanding talent through the 'Junior KAIST' and '3+4 TUBE Programs.' While setting the direction for regional university innovation through the specialized and performance-centric 'KAIST Model,' the university is also taking the lead in popularizing science and fulfilling its social responsibilities.
President Kwang Hyung Lee emphasized, "We will continue to pursue the expansion of the AI research budget and the establishment of international joint research infrastructure through close cooperation with the government." He concluded, "We will cultivate young talents who have chosen the future to be the main players in South Korean science and technology, fulfilling our central role in the 'AI Powerhouse Republic of Korea,' where the nation and the regions grow together."
2025.12.09 View 2861 -
KAIST Removes 99.9% of Ultrafine Dust Using Nano Water Droplet Technology
<(From Left) Ph.D candidate Sungyoon Woo, Professor Il-Doo Kim, Professor Seung S.Lee, Ph.D candiate Jihwan Chae, Researcher Jiyeon Yu, (Upper Right) Dr. Yujang Cho>
A KAIST research team has drawn attention by developing a new water-based air purification technology that combines “nano water droplets that capture dust” with a “nano sponge structure that autonomously draws up water,” enabling dust removal using nano water droplets without filters, self-supplied water operation, and long-term, quiet, and safe performance.
KAIST (President Kwang Hyung Lee) announced on the December 8 that a joint research team led by Professor Il-Doo Kim of the Department of Materials Science and Engineering and Professor Seung S. Lee of the Department of Mechanical Engineering developed a new water electrospray–based air purification device that rapidly removes ultrafine dust without filters, generates no ozone, and operates with ultra-low power consumption.
The research team confirmed that this device overcomes the limitations of conventional air purifiers by eliminating the need for filter replacement, producing no ozone, and removing even extremely fine ultrafine dust as small as PM0.3 (diameter 0.3 μm), which is about 1/200 the thickness of a human hair, within a short time. In addition, it demonstrated high stability and durability without performance degradation even during long-term use.
This device was created by combining Professor Seung S. Lee’s “ozone-free water electrospray” technology with Professor Il-Doo Kim’s “hygroscopic nanofiber Emitter” technology.
Inside the device are a high-voltage electrode, a nanofiber absorber that autonomously draws up water, and polymer microchannels that transport water via capillary action. Thanks to this structure, a self-pumped configuration is achieved in which water is automatically supplied without a pump, enabling stable long-term water electrospray operation.
Tests conducted by the research team in a 0.1 m3 experimental chamber showed that the device removed 99.9% of various particles in the PM0.3–PM10 range within 20 minutes. In particular, it exhibited outstanding performance by removing 97% of PM0.3 ultrafine dust, which is difficult to eliminate using conventional filter-based air purifiers, within just 5 minutes.
Even after 30 consecutive tests and 50 hours of continuous operation, the device operated stably without performance degradation, and its power consumption was approximately 1.3 W, which is lower than that of a smartphone charger and only about 1/20 that of conventional HEPA (High Efficiency Particulate Air) filter–based air purifiers.
In addition, because there is no filter, there is no pressure loss in airflow and almost no noise is generated.
This technology maintains high-efficiency purification performance while generating no ozone at all, presenting the potential for a next-generation eco-friendly air purification platform.
In particular, with advantages such as elimination of filter replacement costs, ultra-low power operation, and secured long-term stability, it is expected to expand into various fields including indoor environments as well as automotive, cleanroom, portable, and wearable air purification modules.
Commercialization of this technology is currently underway through A2US Co., Ltd., a university spin-off company from Professor Seung S. Lee’s laboratory.
A2US Co., Ltd. won a CES 2025 Innovation Award and plans to launch a portable air purifier product in 2026. The product is equipped not only with fine dust removal using nano water droplets but also with odor removal and pathogen sterilization functions.
<Figure1.Design and Operating Mechanism of a Miniature Air-Purification Device Based on Cone-Jet Water Electrospray Using a Self-Pumping Hygroscopic (PVA–PAA–MMT) Nanofiber Membrane (PPM-NFM) Emitter.>
<Figure 2. (a) Schematic of the Self-Pumping Hygroscopic Nanofiber Membrane (PPM-NFM) Emitter, and (b) Corresponding Photograph and Surface Scanning Microscopy Images.>
This research was conducted with Jihwan Chae (Ph.D. candidate, Department of Mechanical Engineering, KAIST) and Yujang Cho (Ph.D., Department of Materials Science and Engineering, KAIST) as co–first authors, and with Professor Seung S. Lee (Department of Mechanical Engineering) and Professor Il-Doo Kim (Department of Materials Science and Engineering) as corresponding authors. The research results were published on November 14 in the international journal Advanced Functional Materials (AFM), published by Wiley, a world-renowned publisher in materials science and nanotechnology.
※ Paper title: “Self-Pumped Hygroscopic Nanofiber Emitter for Ozone-Free Water Electrospray-Based Air Purification,” DOI: 10.1002/adfm.202523456
This research was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, and the KAIST–MIT Future Energy Frontier Research Center (AI-robotics–based energy materials innovation) program.
2025.12.08 View 2717 -
KAIST, Production Temperature ↓ by 500°C, Power Output ↑ 2x… Next-Generation Ceramic Electrochemical Cell Reborn
<(Top row, from left) Professor Kang Taek Lee, Ph.D candidate Yejin Kang, Dr. Dongyeon Kim, (Bottom row, from left) M.S candidate Mincheol Lee, Ph.D candidate Seeun Oh, Ph.D candidate Seungsoo Jang, Ph.D candidate Hyeonggeun Kim>
As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world.
KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave + vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components.
The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance.
As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world.
KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave + vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components.
The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance.
To solve this, the research team devised a new heat-treatment method called “vapor-phase diffusion.” This technique places a special auxiliary material (a vapor source) next to the cell and irradiates it with microwaves to quickly diffuse vapor. When the temperature reaches approximately 800°C, the vapor released from the auxiliary material moves toward the electrolyte and tightly bonds the ceramic particles. Thanks to this technology, a process that previously required 1,500°C can now be completed at just 980°C. In other words, a world-first ceramic electrochemical cell fabrication technology has been created that produces high-performance electricity at a “low temperature” without damaging the electrolyte.
A cell fabricated with this process produced 2 W of power stably from a 1 cm² cell (roughly the size of a fingernail) at 600°C and generated 205 mL of hydrogen per hour at 600°C (about the volume of a small paper cup, among the highest in the industry). It also maintained stability without performance degradation during 500 hours of continuous operation.
In other words, this technology reduces the production temperature (−500°C), lowers the operating temperature (600°C), doubles performance (2 W/cm²), and extends the lifespan (500-hour stability), achieving world-class performance in ceramic cell technology.
The research team also enhanced the reliability of the technology by using digital twins (virtual simulations) to analyze gas-transport phenomena occurring in the microscopic internal structure of the cell − phenomena that are difficult to observe in actual experiments.
<Figure 1. (a) Schematic of the vapor-diffusion-based process; (b) Surface microstructure of the electrolyte; (c) Internal barium composition ratio of the electrolyte according to processing conditions; (d) Comparison of power-generation performance with previous studies>
< Figure 2. (a) Three-dimensional reconstructed image of the protonic ceramic electrochemical cell fuel electrode according to processing conditions (b) Pore structure (c) Gas-transport simulation results >
Professor Kang Taek Lee emphasized, “This study is the world’s first case of using vapor to lower the heat-treatment temperature by more than 500°C while still producing a high-performance, high-stability cell.” He added, “It is expected to become a key manufacturing technology that addresses the power challenges of the AI era and accelerates the hydrogen society.”
Dongyeon Kim (KAIST PhD) and Yejin Kang (KAIST PhD candidate) participated as co–first authors. The research results were published in Advanced Materials (IF: 26.8), one of the world’s leading journals in energy and materials science, and were selected as the Inside Front Cover article on October 29.
(Paper title: “Sub-1000°C Sintering of Protonic Ceramic Electrochemical Cells via Microwave-Driven Vapor Phase Diffusion,” DOI: https://doi.org/10.1002/adma.202506905)
This research was supported by the MSIT’s Mid-career Researcher Program and the H2 Next Round Program.
2025.12.05 View 2641 -
KAIST, National Quantum Fab Research Institute Opening Ceremony and Research Building Groundbreaking Ceremony Held
<Groundbreaking Ceremony Shovel Scene for the KAIST National Quantum Fab Research Building>
KAIST announced on December 3rd that it held the opening ceremony for the National Quantum Fab Research Institute and the groundbreaking ceremony for the Quantum Fab Research Building at the KAIST main campus in Daejeon, officially commencing the construction of the nation's core infrastructure to enhance South Korea's quantum technology competitiveness.
The event began with a progress report and introduction of the institute by Yong Hoon Cho, Director of the Quantum Fab Research Institute, followed by a groundbreaking ceremony to mark the official start of the Quantum Fab Research Building's construction and an unveiling of the plaque. Approximately 50 officials attended the event, including Jang-woo Lee, Mayor of Daejeon, Kwang Hyung Lee, President of KAIST, and the presidents of the National Nanofab Center and the Korea Research Institute of Standards and Science, representing government, local government, and collaborating organizations.
<Plaque-Unveiling Scene at the Opening of the KAIST National Quantum Fab Research Institute>
Since being selected as the lead institution for the Quantum Fab in a competition held by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation last year, our university secured a commitment of 20 billion KRW from the Daejeon Metropolitan City for construction costs and completed the institute's establishment and design. The new Quantum Fab Research Building, with a total floor area of 2,498 ㎡, is targeted for completion in 2027.
The new building will house South Korea's largest specialized, open-access cleanroom fab for quantum devices. A total of 45 billion KRW or more will be invested by 2031, including national funds, local government funds, and KAIST's budget. Over 37 units of advanced equipment will be installed in the 1st and 3rd-floor FAB cleanrooms in stages, along with stability facilities such as Class 100-1,000 cleanliness standards, constant temperature/humidity, and emergency power supply.
The KAIST Quantum Fab operates on a fully open-access system allowing researchers to directly carry out processes. It will support processing technologies for various quantum platforms, including photons, point defects, and neutral atoms, and will also enhance user programs such as training and workshops. Phase 1 service began in July of this year, and Phase 2 full-scale operation, based on the newly installed equipment, will start in 2028.
Jang-woo Lee, Mayor of Daejeon, stated, "The KAIST open-access Quantum Fab is a core platform that will lead the industrialization of quantum technology in South Korea," adding, "Especially since the US and South Korea have designated quantum computing as a strategic field in their $350 billion technology cooperation package, Daejeon's role is becoming even more crucial."
Director Yong-Hoon Cho said, "Through a user-centric process support system, we will play a central role in the national quantum research ecosystem," adding, "Based on our research capabilities and support system, we will expand industry-academia-research cooperation and aim to leap forward as a pilot quantum fab."
President Kwang Hyung Lee remarked, "Quantum science and technology is a core strategic area that will determine the future technological hegemony," and "We will take this opening and groundbreaking ceremony as an opportunity for industry, academia, research, and government to join forces and strengthen the competitiveness of the national quantum ecosystem."
KAIST plans to focus on establishing a self-sustainable virtuous cycle system centered around the Quantum Fab, and will further dedicate efforts to enhancing national strategic technology competitiveness through the nurturing of specialized talent and the development of processing technologies for each platform.
<Bird’s Eye View of the KAIST National Quantum Fab Research Building>
2025.12.04 View 2984 -
KAIST Unveils Cause of Performance Degradation in Electric Vehicle High-Nickel Batteries: "Added with Good Intentions
<(From left in the front row) Professor Nam-Soon Choi, Professor Dong-Hwa Seo, (back row, from left) Ph.D candidate Gihoon Lee, Ph.D candidate Seung Hee Han, Ph.D candidate Jae-Seung Kim, (top) M.S candidate Junyoung Kim>
High-nickel batteries, which are high-energy lithium-ion batteries primarily used in electric vehicles, offer high energy density but suffer from rapid performance degradation. A research team from KAIST has, for the first time globally, identified the fundamental cause of the rapid deterioration (degradation) of high-nickel batteries and proposed a new approach to solve it.
KAIST announced on December 3rd that a research team led by Professor Nam-Soon Choi of the Department of Chemical and Biomolecular Engineering, in collaboration with a research team led by Professor Dong-Hwa Seo of the Department of Materials Science and Engineering, has revealed that the electrolyte additive 'succinonitrile (CN4), which has been used to improve battery stability and lifespan, is actually the key culprit causing performance degradation in high-nickel batteries.
In a battery, electricity is generated as lithium ions travel between the cathode and the anode. A small amount of CN4 is included in the electrolyte to facilitate the movement of lithium. The research team confirmed through computer calculations that CN4, which has two nitrile (-CN) structures, attaches excessively strongly to the nickel ions on the surface of the high-nickel cathode.
The nitrile structure is a 'hook-like' structure, where carbon and nitrogen are bound by a triple bond, making it adhere well to metal ions. This strong bonding destroys the protective electrical double layer (EDL) that should form on the cathode surface. During the charging and discharging process, the cathode structure is distorted (Jahn-Teller distortion), and even electrons from the cathode are drawn out to the CN4, leading to rapid damage of the cathode.
Nickel ions that leak out during this process migrate through the electrolyte to the anode surface, where they accumulate. This nickel acts as a 'bad catalyst' that accelerates electrolyte decomposition and wastes lithium, further speeding up battery degradation.
Various analyses confirmed that CN4 transforms the high-nickel cathode surface into an abnormal layer deficient in nickel, and changes the normally stable structure into an abnormal 'rock-salt structure'.
This proves the dual nature of CN4: while useful in LCO batteries (lithium cobalt oxide), it actually causes the structural collapse in high-nickel batteries with a high nickel ratio.
This research holds significant meaning as a precise analysis that goes beyond simple control of charging/discharging conditions, to even elucidating the actual electron transfer occurring between metal ions and electrolyte molecules. Based on this achievement, the research team plans to develop a new electrolyte additive optimized for high-nickel cathodes.
<Schematic diagram of the ligand coordination between CN₄ molecules and Ni³⁺ on the high-nickel cathode surface and the cathode structural degradation process>
Professor Nam-Soon Choi stated, "A precise, molecular-level understanding is essential to enhance battery lifespan and stability. This research will pave the way for the development of new additives that do not excessively bond with nickel, significantly contributing to the commercialization of next-generation high-capacity batteries."
This research, jointly led by Professor Nam-Soon Choi, Seung Hee Han, Junyoung Kim, and Gihoon Lee of the Department of Chemical and Biomolecular Engineering, and Professor Dong-Hwa Seo and Jae-Seung Kim of the Department of Materials Science and Engineering as co-first authors, was published online on November 14th in the prestigious international journal 'ACS Energy Letters' and was selected as the cover article.
※ Paper Title: Unveiling Bidentate Nitrile-Driven Structural Degradation in Ultra-High-Nickel Cathodes,
https://doi.org/10.1021/acsenergylett.5c02845
<Cover Page of International Journal(ACS Energy Letters)>
The research was supported by Samsung SDI.
2025.12.03 View 2722 -
Success in Measuring Nano Droplets, A New Breakthrough in Hydrogen, Semiconductor, and Battery Research
<(From Left) Ph.D candidate Uichang Jeong, Professor Seungbum Hong>
In hydrogen production catalysts, water droplets must detach easily from the surface to prevent blockage by bubbles, allowing for faster hydrogen generation. In semiconductor manufacturing, the quality of the process is determined by how evenly water or liquid spreads on the surface, or how quickly it dries. However, directly observing how such water or liquid spreads and moves on a surface ('wettability') at the nanoscale has been technically almost impossible until now, forcing researchers to rely mostly on conjecture. KAIST announced on December 2nd that a research team led by Professor Seungbum Hong of the Department of Materials Science and Engineering, in collaboration with Professor Jongwoo Lim's team at Seoul National University, has developed a technology to directly observe nano-sized water droplets in real-time using an Atomic Force Microscope (AFM) and to calculate the contact angle based on the droplet's shape. This research, by enabling the visual confirmation of the actual shape of nano-droplets, allows for the precise analysis of how well water droplets adhere to and detach from a surface. This is expected to be immediately applicable to various advanced technologies where liquid movement determines performance, such as hydrogen production catalysts, fuel cells, batteries, and semiconductor processes. Recently, precise measurement at the nanoscale has become crucial for wettability analysis technology. Traditional methods using large water droplets, several millimeters in size, could distinguish between hydrophilicity (where water spreads easily) and hydrophobicity (where water doesn't spread easily) on the surface. However, at the nanoscale, the droplets are too small to directly observe their shape. The research team successfully induced nano-droplets to form naturally by gently cooling the surface to a temperature where atmospheric water vapor does not freeze. They then observed these droplets using the non-contact mode of the AFM to capture their original shape. Since nano-droplets are sensitive and can be deformed by mere contact with the probe, precise control is essential. Furthermore, when the team applied this technique to the ferroelectric material lithium tantalate, they were the first to confirm a difference in the nano-droplet contact angle depending on the material's electrical direction (polarization). This difference, which was not visible with large droplets, demonstrates that nano-droplets are highly sensitive to the electrical state of the surface. The team then applied this technology to the water electrolysis catalyst used in hydrogen production, observing a single nano-droplet. This result aids in understanding how water reacts on the catalyst surface and can be used to analyze catalyst performance, particularly how well bubbles detach.
<Figure 1. Nanoscale droplet visualization using non-contact mode>
<Figure 2. Single-droplet visualization formed on sub-micron-sized water-splitting catalyst LiFeLDH particles>
Professor Seungbum Hong stated, "This research is an important case demonstrating that the Atomic Force Microscope can be used to directly visualize nano-sized water droplets and even measure the contact angle. Being able to observe the behavior of water droplets in the nano-world, which was previously invisible, will establish this as a core analysis technology for the development of next-generation energy and electronic materials." This research, in which Uichang Jeong, a PhD candidate in the KAIST Department of Materials Science and Engineering, participated as the first author, was published on October 17th in 'ACS Applied Materials and Interfaces', a prestigious journal in the field of new materials and chemical engineering published by the American Chemical Society (ACS).
Paper Title: Nanoscale Visualization and Contact Angle Analysis of Water Droplets on Ferroelectric Materials
DOI: https://doi.org/10.1021/acsami.5c14404
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.12.03 View 1797 -
KAIST Suppresses Side Effects of mRNA Therapeutics, Broadly Applicable Platform for Safer, Personalized Treatments
<(From Left) Professor Yong Woong Jun, Ph.D candidate Tae Ung Jeong, Ph.D candidate Jihun Choi>
mRNA, widely known from the COVID-19 vaccine, is not actually a “therapeutic agent,” but a technology that delivers the blueprint for functional proteins into the body so that induces therapeutic effects. Recently, its application has expanded to cancer and genetic disease treatments, but mRNA therapeutics have caused serious side effects such as pulmonary embolism, stroke, thrombosis, and autoimmune diseases because proteins are excessively produced all at once immediately after administration. Although technology to control the endogenous protein factory has been continuously needed, there had been no suitable solution.
KAIST (President Kwang Hyung Lee) announced on the 1st of December that Professor Yong Woong Jun’s research team in the Department of Chemistry has proposed a new strategy that can control the initiation timing and rate at which mRNA produces proteins. By using this method, the rate of protein production can be adjusted/personalized according to a patient’s condition, enabling safer treatment.
This technology is expected to serve as an important turning point in next-generation mRNA therapeutics, not only fundamentally reducing side effects of mRNA treatments but also enabling application to treatment areas requiring precise protein regulation such as stroke, cancer, and immune diseases.
For a protein to be produced, the cell’s “protein production machinery (ribosomes and initiation factors)” must attach to the mRNA blueprint and begin working. The research team focused on the fact that delaying this process even slightly can prevent the sudden surge of protein production.
Therefore, instead of using complex technologies, they developed a simple method in which intentionally slightly damaged DNA fragments are attached to mRNA. These DNA fragments act like a small “shield,” preventing the protein production machinery from immediately attaching to the mRNA and thereby gently slowing the initiation speed of protein production.
The damaged DNA used here is a safe biological material naturally recycled in the body and is very inexpensive. Because it only needs to be mixed with mRNA right before injection, it is suitable for real-world medical use.
As time passes, the body’s natural “repair enzymes” partially degrade the damaged DNA, and during this process, the structure attached to the mRNA is released, smoothly transitioning the protein production speed back to normal mode. As a result, the previous risk of proteins being explosively produced all at once is greatly reduced.
The research team confirmed that by adjusting the length and degree of damage of the DNA, they could precisely design when and how slowly protein production would begin. They also found that even when multiple types of mRNA are administered at once, the proteins can be produced sequentially in the desired order, meaning this method could innovate existing approaches that required multiple separate injections for complex treatments.
This technology was selected by KAIST as one of its “Future Promising Core Technologies” and was also introduced at the “2025 KAIST Techfair Technology Transfer Session.”
<A translation-control strategy based on DNA–mRNA hybrids. The damaged base (in red) is removed by a repair enzyme, after which the DNA and mRNA dissociate, allowing translation factors and ribosomes to bind and initiate protein translation>
Professor Yong Woong Jun said, “Biological phenomena are ultimately chemistry, so we were able to precisely control the protein production process through a chemical approach,” and added that “this technology not only enhances the safety of mRNA therapeutics but also provides a foundation for expanding into precision treatments tailored to various diseases such as cancer and genetic disorders.”
The results of this research, with Jihun Choi (KAIST, 3rd-year PhD student) and Tae Ung Jeong (KAIST, 1st-year PhD student) participating as co–first authors, were published on November 6 in Angewandte Chemie International Edition, one of the most prestigious journals in the field of chemistry.
※ Paper title: “Harnessing Deaminated DNA to Modulate mRNA Translation for Controlled and Sequential Protein Expression,” Authors: Jihun Choi (KAIST, co–first author), Tae Ung Jeong (KAIST, co–first author), and Yong Woong Jun (KAIST, corresponding author), among a total of 10 authors, DOI: 10.1002/anie.202516389
This study was supported by the National Research Foundation of Korea (NRF) through the Excellent Young Researcher Program.
2025.12.02 View 2257 -
AI Technology World No. 1 in Finding the Exact Moment in a Video: Where is the First Place?
< (From left) Professor Joon Hyuk Noh (Assistant Professor, Department of Artificial Intelligence, Ewha Womans University), Seojin Hwan, Yoonki Cho (Ph.D. Candidate), Professor Sung-Eui Yoon (School of Computing, KAIST) >
When faced with a complex question like 'What object disappeared while the camera was pointing elsewhere?', a common problem is that AI often relies on language patterns to guess a 'plausible answer,' instead of actually observing the real situation in the video. To overcome this limitation, our university's research team developed a technology that enables the AI to autonomously identify the 'exact critical moment (Trigger moment)' within the video, and the team’s excellence was proven by winning an international AI competition with this technology. The university announced on the 28th that the research team led by Professor Sung-Eui Yoon from the School of Computing, in collaboration with Professor Joon Hyuk Noh's team from Ewha Womans University, took 1st place in the Grounded Video Question Answering track of the Perception Test Challenge held at ICCV 2025, a world-renowned computer vision conference. The Perception Test Challenge held at ICCV 2025 was organized by Google DeepMind with a total prize pool of 50,000 Euros (approximately 83 million KRW). It assesses the cognitive and reasoning abilities of multimodal AI, which must comprehensively understand various data, including video, audio, and text. Crucially, the core evaluation factor is the ability to make judgments based on actual video evidence, moving beyond language-centric bias. Unlike conventional methods that analyze the entire video indiscriminately, our university's research team developed a new technology that instructs the AI to first locate the core scene (Trigger moment) essential for finding the correct answer. Simply put, this technology is designed to make the AI autonomously determine: “This scene is decisive for answering this question!” The research team calls this framework CORTEX (Chain-of-Reasoning for Trigger Moment Extraction). The research team's system consists of a three-stage structure where three models performing different functions operate sequentially. First, the Reasoning AI (Gemini 2.5 Pro) reasons about which moment is required to answer the question and finds candidate Trigger moments. Next, the Object Location Finding Model (Grounding Model, Molmo-7B) accurately identifies the exact location (coordinates) of people, cars, and objects on the screen during the selected moment. Finally, the Tracking Model (SAM2) precisely tracks the movement of objects in the time frame before and after the selected scene, using that scene as a reference, thereby reducing errors. In short, the 'method of accurately pinpointing a key scene and tracking the evidence for the answer centered on that scene' significantly reduced problems like initial misjudgment or occlusion in the video. In the Grounded Video Question Answering (Grounded VideoQA) track, which saw 23 participating teams, the KAIST team SGVR Lab (Scalable Graphics, Vision & Robotics Lab) recorded 0.4968 points in the HOTA (Higher Order Tracking Accuracy) metric, overwhelmingly surpassing the 2nd place score of 0.4304 from Columbia University, USA, to secure 1st place. This achievement is nearly double the previous year's winning score of 0.2704 points. This technology has wide-ranging applications in real-life settings. Autonomous driving vehicles can accurately identify moments of potential accident risk, robots can understand the surrounding environment smarter, security and surveillance systems can rapidly locate critical scenes, and media analysis can precisely track the actions of people or objects in chronological order. This is a core technology that enables AI to judge based on "actual evidence in the video." The ability to accurately pinpoint how objects behave over time in a video is expected to greatly expand the application of AI in real-world scenarios in the future.
< Pipeline image of the grounding framework for video question answering proposed by the research team >
This research was presented on October 19th at ICCV 2025, the 3rd Perception Test Challenge conference. The achievement was supported by the Ministry of Science and ICT's Basic Research Program (Mid-Career Researcher), the SW Star Lab Project's 'Development of Perception, Action, and Interaction Algorithms for Open-World Robot Services,' and the AGI Project's 'Reality Construction and Bi-directional Capability Approach based on Cognitive Agents for Embodied AGI' tasks."
2025.12.01 View 2236