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KAIST Researchers Unveil an AI that Generates "Unexpectedly Original" Designs
< Photo 1. Professor Jaesik Choi, KAIST Kim Jaechul Graduate School of AI >
Recently, text-based image generation models can automatically create high-resolution, high-quality images solely from natural language descriptions. However, when a typical example like the Stable Diffusion model is given the text "creative," its ability to generate truly creative images remains limited. KAIST researchers have developed a technology that can enhance the creativity of text-based image generation models such as Stable Diffusion without additional training, allowing AI to draw creative chair designs that are far from ordinary.
Professor Jaesik Choi's research team at KAIST Kim Jaechul Graduate School of AI, in collaboration with NAVER AI Lab, developed this technology to enhance the creative generation of AI generative models without the need for additional training.
< Photo 2. Gayoung Lee, Researcher at NAVER AI Lab; Dahee Kwon, Ph.D. Candidate at KAIST Kim Jaechul Graduate School of AI; Jiyeon Han, Ph.D. Candidate at KAIST Kim Jaechul Graduate School of AI; Junho Kim, Researcher at NAVER AI Lab >
Professor Choi's research team developed a technology to enhance creative generation by amplifying the internal feature maps of text-based image generation models. They also discovered that shallow blocks within the model play a crucial role in creative generation. They confirmed that amplifying values in the high-frequency region after converting feature maps to the frequency domain can lead to noise or fragmented color patterns. Accordingly, the research team demonstrated that amplifying the low-frequency region of shallow blocks can effectively enhance creative generation.
Considering originality and usefulness as two key elements defining creativity, the research team proposed an algorithm that automatically selects the optimal amplification value for each block within the generative model.
Through the developed algorithm, appropriate amplification of the internal feature maps of a pre-trained Stable Diffusion model was able to enhance creative generation without additional classification data or training.
< Figure 1. Overview of the methodology researched by the development team. After converting the internal feature map of a pre-trained generative model into the frequency domain through Fast Fourier Transform, the low-frequency region of the feature map is amplified, then re-transformed into the feature space via Inverse Fast Fourier Transform to generate an image. >
The research team quantitatively proved, using various metrics, that their developed algorithm can generate images that are more novel than those from existing models, without significantly compromising utility.
In particular, they confirmed an increase in image diversity by mitigating the mode collapse problem that occurs in the SDXL-Turbo model, which was developed to significantly improve the image generation speed of the Stable Diffusion XL (SDXL) model. Furthermore, user studies showed that human evaluation also confirmed a significant improvement in novelty relative to utility compared to existing methods.
Jiyeon Han and Dahee Kwon, Ph.D. candidates at KAIST and co-first authors of the paper, stated, "This is the first methodology to enhance the creative generation of generative models without new training or fine-tuning. We have shown that the latent creativity within trained AI generative models can be enhanced through feature map manipulation."
They added, "This research makes it easy to generate creative images using only text from existing trained models. It is expected to provide new inspiration in various fields, such as creative product design, and contribute to the practical and useful application of AI models in the creative ecosystem."
< Figure 2. Application examples of the methodology researched by the development team. Various Stable Diffusion models generate novel images compared to existing generations while maintaining the meaning of the generated object. >
This research, co-authored by Jiyeon Han and Dahee Kwon, Ph.D. candidates at KAIST Kim Jaechul Graduate School of AI, was presented on June 16 at the International Conference on Computer Vision and Pattern Recognition (CVPR), an international academic conference.* Paper Title: Enhancing Creative Generation on Stable Diffusion-based Models* DOI: https://doi.org/10.48550/arXiv.2503.23538
This research was supported by the KAIST-NAVER Ultra-creative AI Research Center, the Innovation Growth Engine Project Explainable AI, the AI Research Hub Project, and research on flexible evolving AI technology development in line with increasingly strengthened ethical policies, all funded by the Ministry of Science and ICT through the Institute for Information & Communications Technology Promotion. It also received support from the KAIST AI Graduate School Program and was carried out at the KAIST Future Defense AI Specialized Research Center with support from the Defense Acquisition Program Administration and the Agency for Defense Development.
2025.06.20 View 88 -
KAIST Develops Glare-Free, Heat-Blocking 'Smart Window'... Applicable to Buildings and Vehicles
• Professor Hong Chul Moon of the Department of Chemical and Biomolecular Engineering develops RECM, a next-generation smart window technology, expecting cooling energy savings and effective indoor thermal management.
• When using the developed RECM, a significantly superior temperature reduction effect is observed compared to conventional windows.
• With a 'pedestrian-friendly smart window' design that eliminates glare by suppressing external reflections, it is expected to be adapted in architectural structures, transportation, and more.
< (From left) First author Hoy Jung Jo, Professor Hong Chul Moon >
In the building sector, which accounts for approximately 40% of global energy consumption, heat ingress through windows has been identified as a primary cause of wasted heating and cooling energy. Our research team has successfully developed a 'pedestrian-friendly smart window' technology capable of not only reducing heating and cooling energy in urban buildings but also resolving the persistent issue of 'light pollution' in urban living.
On the 17th of June, Professor Hong Chul Moon's research team at KAIST's Department of Chemical and Biomolecular Engineering announced the development of a 'smart window technology' that allows users to control the light and heat entering through windows according to their intent, and effectively neutralize glare from external sources.
Recently, 'active smart window' technology, which enables free adjustment of light and heat based on user operation, has garnered significant attention. Unlike conventional windows that passively react to changes in temperature or light, this is a next-generation window system that can be controlled in real-time via electrical signals.
The next-generation smart window technology developed by the research team, RECM (Reversible Electrodeposition and Electrochromic Mirror), is a smart window system based on a single-structured *electrochromic device that can actively control the transmittance of visible light and near-infrared (heat).
*Electrochromic device: A device whose optical properties change in response to an electrical signal.
In particular, by effectively suppressing the glare phenomenon caused by external reflected light—a problem previously identified in traditional metal *deposition smart windows—through the combined application of electrochromic materials, a 'pedestrian-friendly smart window' suitable for building facades has been realized.
*Deposition: A process involving the electrochemical reaction to coat metal ions, such as Ag+, onto an electrode surface in solid form.
The RECM system developed in this study operates in three modes depending on voltage control.
Mode I (Transparent Mode) is advantageous for allowing sunlight to enter the indoor space during winter, as it transmits both light and heat like ordinary glass.
In Mode II (Colored Mode), *Prussian Blue (PB) and **DHV+• chemical species are formed through a redox (oxidation-reduction) reaction, causing the window to turn a deep blue color. In this state, light is absorbed, and only a portion of the heat is transmitted, allowing for privacy while enabling appropriate indoor temperature control.
*Prussian Blue: An electrochromic material that transitions between colorless and blue upon electrical stimulation.
**DHV+•: A radical state colored molecule generated upon electrical stimulation.
Mode III (Colored and Deposition Mode) involves the reduction and deposition of silver (Ag+) ions on the electrode surface, reflecting both light and heat. Concurrently, the colored material absorbs the reflected light, effectively blocking glare for external pedestrians.
The research team validated the practical indoor temperature reduction effect of the RECM technology through experiments utilizing a miniature model house. When a conventional glass window was installed, the indoor temperature rose to 58.7°C within 45 minutes. Conversely, when RECM was operated in Mode III, the temperature reached 31.5°C, demonstrating a temperature reduction effect of approximately 27.2°C.
Furthermore, since each state transition is achievable solely by electrical signals, it is regarded as an active smart technology capable of instantaneous response according to season, time, and intended use.
< Figure 1. Operation mechanism of the RECM smart window. The RECM system can switch among three states—transparent, colored, and colored & deposition—via electrical stimulation. At -1.6 V, DHV•+ and Prussian Blue (PB) are formed, blocking visible light to provide privacy protection and heat blocking. At -2.0 V, silver (Ag) is deposited on the electrode surface, reflecting light and heat, while DHV•+ and Prussian Blue absorb reflected light, effectively suppressing external glare. Through this mechanism, it functions as an active smart window that simultaneously controls light, heat, and glare. >
Professor Hong Chul Moon of KAIST, the corresponding author of this study, stated, "This research goes beyond existing smart window technologies limited to visible light control, presenting a truly smart window platform that comprehensively considers not only active indoor thermal control but also the visual safety of pedestrians." He added, "Various applications are anticipated, from urban buildings to vehicles and trains."
< Figure 2. Analysis of glare suppression effect of conventional reflective smart windows and RECM. This figure presents the results comparing the glare phenomenon occurring during silver (Ag) deposition between conventional reflective smart windows and RECM Mode III. Conventional reflective devices resulted in strong reflected light on the desk surface due to their high reflectivity. In contrast, RECM Mode III, where the colored material absorbed reflected light, showed a 33% reduction in reflected light intensity, and no reflected light was observed from outside. This highlights the RECM system's distinctiveness and practicality as a 'pedestrian-friendly smart window' optimized for dense urban environments, extending beyond just heat blocking. >
The findings of this research were published on June 13, 2025, in Volume 10, Issue 6 of 'ACS Energy Letters'. The listed authors for this publication are Hoy Jung Jo, Yeon Jae Jang, Hyeon-Don Kim, Kwang-Seop Kim, and Hong Chul Moon.
※ Paper Title: Glare-Free, Energy-Efficient Smart Windows: A Pedestrian-Friendly System with Dynamically Tunable Light and Heat Regulation
※ DOI: 10.1021/acsenergylett.5c00637
< Figure 3. Temperature reduction performance verification in a miniature model house. The actual heat blocking effect was evaluated by applying RECM devices to a model building. Under identical conditions, the indoor temperature with ordinary glass rose to 58.7°C, whereas with RECM in Mode III, it reached 31.5°C, demonstrating a maximum temperature reduction effect of 27.2°C. The indoor temperature difference was also visually confirmed through thermal images, which proves the potential for indoor temperature control in urban buildings. >
This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and the internal research program of the Korea Institute of Machinery and Materials.
2025.06.20 View 487 -
‘InnoCORE Research Group’ Launched to Lead AI Convergence Innovation
KAIST announced on the 16th of June that it has launched the ‘InnoCORE (Innovation-Core) Research Group,’ which will lead advanced strategic research in AI convergence (AI+S&T), in cooperation with the Ministry of Science and ICT (Minister Yoo Sang-im, hereinafter referred to as MSIT) and DGIST, GIST, and UNIST*. Through this, the group plans to actively recruit up to 200 world-class postdoctoral researchers.
DGIST (Daegu Gyeongbuk Institute of Science & Technology), GIST (Gwangju Institute of Science & Technology), UNIST (Ulsan National Institute of Science and Technology)
The ‘InnoCORE Research Group’ aims to foster core research personnel who will lead innovation in the field of AI convergence, focusing on nurturing and attracting high-level research talent in AI+Science & Technology. This is a strategic response to prevent brain drain of domestic talent and attract excellent overseas talent amidst the accelerating global competition for AI talent.
Through this initiative, our university plans to accelerate AI-based science and technology innovation and disseminate research achievements across industries and the economy by supporting top domestic and international postdoctoral researchers to dedicate themselves to developing AI convergence technologies in an advanced collaborative research environment.
The InnoCORE project for advanced AI+S&T convergence research and global talent attraction is jointly promoted by four science and technology institutes, including KAIST. It is structured around AI core technologies (such as hyper-scale language models, AI semiconductors) and AI convergence technologies (such as bio, manufacturing, energy, and aerospace).
As the leading institution, our university operates the following four research groups:
Hyper-scale Language Model Innovation Research Group: Advancement of LLM technology and research on generative AI, multimodal AI, and ensuring reliability.
AI-based Intelligent Design-Manufacturing Integration Research Group: Establishment of an AI platform for the entire lifecycle of the manufacturing industry and innovation in design and processes.
AI-Innovation Drug Research Group: Securing AI-based drug development technologies across the entire lifecycle and overcoming intractable diseases.
AI-Transformed Aerospace Research Group: AI transformation of aerospace systems throughout their lifecycle and development of new technologies such as autonomous flight and space communication.
< Poster on the InnoCORE Global Jobfair for Recruitment of Postdoctoral Researchers >
In addition, a total of eight research groups are formed to promote global collaborative convergence research, including those led by DGIST, GIST, and UNIST: ▲Bio-Integrated Physical AI, ▲Early Diagnosis of Brain Diseases AI+Nano Convergence, ▲Intelligent Hydrogen Technology Innovation, and ▲AI-Space Solar Power Research Group.
Starting in 2025, the four science and technology institutes, including KAIST, will officially begin recruiting 400 postdoctoral researchers in the AI+S&T fields. Selected postdoctoral researchers will be guaranteed high-level treatment with an annual salary of over 90 million KRW, and additional support through matching with companies and research projects is also planned.
In particular, global recruitment fairs will be held in major US regions to expand the attraction of excellent overseas talent. Local recruitment fairs will be held in Boston (Harvard, MIT), New York (NYU), and Silicon Valley (Stanford) in June, along with promotions through global academic journals such as Nature and Science, and LinkedIn.
KAIST plans to provide multiple mentor programs, global joint research opportunities, and excellent infrastructure (such as supercomputers, semiconductor fabs, and AI research platforms) within the research groups to enable postdoctoral researchers to collaborate with experts from various academic and industrial fields.
President Kwang Hyung Lee emphasized, “Through this InnoCORE project, KAIST will leap forward as a Global Hub for AI+S&T convergence research. Young researchers from around the world will challenge themselves and grow at KAIST, and our country will play a pivotal role in establishing itself as a leading nation in global AI convergence research and industry. To achieve this, we will spare no effort in providing the best research environment and active support.”
KAIST plans to actively pursue the InnoCORE project to secure global competitiveness in AI convergence research and contribute to the development of advanced industries. The eight selected research groups will finalize their detailed research plans by the end of June and commence full-scale research in July.
2025.06.19 View 153 -
“One Experiment Is All It Takes”: KAIST Team Revolutionizes Drug Interaction Testing, Replacing 60,000 Studies
A groundbreaking new method developed by researchers at KAIST and Chungnam National University could drastically streamline drug interaction testing — replacing dozens of traditional experiments with just one.
The research, led by Professor Jae Kyoung Kim of KAIST Department of Mathematical Sciences & IBS Biomedical Mathematics Group and Professor Sang Kyum Kim of Chungnam National University's College of Pharmacy, introduces a novel analysis technique called 50-BOA, published in Nature Communications on June 5, 2025.
< Photo 1. (From left) Professor Sang Kyum Kim (Chungnam National University College of Pharmacy, co-corresponding author), Dr. Yun Min Song (IBS Biomedical Mathematics Group, formerly KAIST Department of Mathematical Sciences, co-first author), undergraduate student Hyeong Jun Jang (KAIST, co-first author), Professor Jae Kyoung Kim (KAIST and IBS Biomedical Mathematics Group, co-corresponding author) (Top left in the bubble) Professor Hwi-yeol Yun (Chungnam National University College of Pharmacy, co-author) >
For decades, scientists have had to repeat drug inhibition experiments across a wide range of concentrations to estimate inhibition constants — a process seen in over 60,000 scientific publications. But the KAIST-led team discovered that a single, well-chosen inhibitor concentration can yield even more accurate results.
< Figure 1. Graphical summary of 50-BOA. 50-BOA improves the accuracy and efficiency of inhibition constant estimation by using only a single inhibitor concentration instead of the traditionally used method of employing multiple inhibitor concentrations. >
“This approach challenges long-standing assumptions in experimental pharmacology,” says Prof. Kim. “It shows how mathematics can fundamentally redesign life science experiments.”
By mathematically analyzing the sources of error in conventional methods, the team found that over half the data typically collected adds no value or even skews results. Their new method not only cuts experimental effort by over 75%, but also enhances reproducibility and accuracy.
To help researchers adopt the method quickly, the team developed a user-friendly tool that takes simple Excel files as input, now freely available on GitHub:
☞ https://github.com/Mathbiomed/50-BOA
< Figure 2. The MATLAB and R package of 50-BOA at GitHub >
The work holds promise for faster and more reliable drug development, especially in assessing potential interactions in combination therapies. The U.S. FDA already emphasizes accurate enzyme inhibition assessment during early-stage drug evaluation — and this method could soon become a new gold standard.
2025.06.16 View 359 -
KAIST Predicts Diseases by Early Detection of Aging Signals in Liver Tissue
- KAIST-KRIBB Develops ‘FiNi-seq’ Technology to Capture Characteristics of Fibrotic Microenvironments Accumulated in Liver Tissue and Dynamic Changes of Early Aging Cells
- Elucidation of the Spatial Ecosystem of Aged Liver Tissue, where Reprogramming of Senescent Cells and Immune Exhaustion Progresses, at the Single-Cell Genome and Epigenome Levels
< (From left) Professor Jong-Eun Park of KAIST Graduate School of Medical Science and Engineering (GSMSE), Dr. Chuna Kim of KRIBB, Dr. Kwon Yong Tak of KAIST GSMSE, Ph.D. Candidate Juyeon Kim of KRIBB, Ph.D. Candidate Myungsun Park of KAIST GSMSE >
Aging and chronic diseases involve the gradual accumulation of subtle tissue changes over a long period. Therefore, there are still limitations in quantitatively understanding these changes within organs and linking them to early signs of disease onset. In response, Korean researchers have successfully developed a platform technology that accurately captures localized changes that first occur within tissue, significantly aiding in faster disease discovery and prediction, and in setting personalized treatment targets.
KAIST (President Kwang Hyung Lee) announced on June 12th that a joint research team led by Professor Jong-Eun Park of the Graduate School of Medical Science and Engineering at KAIST and Dr. Chuna Kim of the Aging Convergence Research Center at the Korea Research Institute of Bioscience and Biotechnology (KRIBB, President Seok-Yoon Kwon) has developed ‘FiNi-seq (Fibrotic Niche enrichment sequencing)’ technology. This technology captures fibrotic microenvironments locally occurring in aged liver tissue and enables precise analysis at the single-cell transcriptome level*.
*Single-cell transcriptome analysis: A method to measure how actively each cell uses which genes, allowing identification and function of individual diseased cells.
The researchers developed a method to selectively enrich early aging microenvironments where regeneration is delayed and fibrosis accumulates, by physically selecting regions with high tissue degradation resistance in aged liver tissue.
In this process, high-resolution identification of fibrosis-related endothelial cells, fibroblasts interacting with the immune system, and immune-exhausted cells such as PD-1 highly expressing CD8 T cells, which were difficult to capture with existing single-cell analysis technologies, was possible.
In particular, the research team confirmed through ‘FiNi-seq’ technology that specific cells observed in fibrotic areas within aged liver tissue secondarily age the surrounding environment through secreted factors, and that this leads to the expansion of the aged environment.
Furthermore, they also elucidated the mechanism by which endothelial cells lose their tissue-specific identity and induce innate immune responses, promoting immune cell infiltration. Through spatial transcriptome analysis, the spatial distribution of fibroblasts interacting with immune cells was quantified, revealing their involvement in tissue regeneration, induction of inflammatory responses, and progression to chronic fibrosis.
The research team performed integrated analysis of multi-omics\* data to obtain transcriptome and epigenome information, precisely interpreting the microenvironment of aged liver tissue and its spatial heterogeneity, and confirming how these changes are connected to the intrahepatic vascular structure.
*Multi-omics: An integrated analysis method for various biological information within an organism, such as genes, proteins, metabolites, and cell information.
The newly developed ‘FiNi-seq’ technology is expected to be a useful platform for high-resolution capture of pathophysiological signals in most chronic liver diseases, including the aging process that causes fibrosis.
< Figure 1. Isolation of fibrotic regions from aged liver tissue, followed by single-cell transcriptome analysis and validation in a fibrosis model. >
The first author, Dr. Kwon Yong Tak of KAIST Graduate School of Medical Science and Engineering (GSMSE), a hepatologist at Seoul St. Mary's Hospital, designed this study to lay the groundwork for early diagnosis and treatment of fibrosis progression, the most important clinical prognostic indicator in chronic liver disease, while pursuing his Ph.D. at KAIST KAIST GSMSE with support from the physician-scientist training program. Co-first author Myungsun Park, a Ph.D. candidate at KAIST KAIST GSMSE, was responsible for the technical implementation of FiNi-seq technology, and Juyeon Kim, a Ph.D. candidate at KRIBB's Aging Convergence Research Center, was responsible for imaging analysis of aged tissue, playing a key role in the research.
Dr. Chuna Kim of KRIBB stated, “Through this study, we were able to precisely elucidate the cellular composition and spatial characteristics of the fibrotic microenvironment observed in aged liver tissue at the single-cell level.”
< Figure 2. Spatially defined stepwise progression patterns of aging-related regions within the liver and identification of regulatory factors inducing them. >
Professor Jong-Eun Park of the Graduate School of Medical Science and Engineering said, “As an analytical technology that can capture subtle changes occurring in the early stages of aging and chronic diseases, it is expected to play a significant role in finding effective treatment targets in the future. Also, we plan to expand this research to chronic diseases in other organs such as the lungs and kidneys, as well as various liver disease models.”
This research was published in the international journal ‘Nature Aging’ on May 5, 2025, with Dr. Kwon Yong Tak of KAIST KAIST GSMSE, Ph.D. Candidate Juyeon Kim of KRIBB, and Ph.D. Candidate Myungsun Park of KAIST as co-first authors.
*Paper Title: Quasi-spatial single-cell transcriptome based on physical tissue properties defines early aging associated niche in liver
*DOI: https://doi.org/10.1038/s43587-025-00857-7
This research was supported by several domestic institutions, including the National Research Foundation of Korea, the Korea Health Industry Development Institute (KHIDI), the Korea Research Institute of Bioscience and Biotechnology (KRIBB), KIST, POSCO Science Fellowship, and the Convergence Medical Scientist Training Program.
2025.06.12 View 425 -
KAIST Turns an Unprecedented Idea into Reality: Quantum Computing with Magnets
What started as an idea under KAIST’s Global Singularity Research Project—"Can we build a quantum computer using magnets?"—has now become a scientific reality. A KAIST-led international research team has successfully demonstrated a core quantum computing technology using magnetic materials (ferromagnets) for the first time in the world.
KAIST (represented by President Kwang-Hyung Lee) announced on the 6th of May that a team led by Professor Kab-Jin Kim from the Department of Physics, in collaboration with the Argonne National Laboratory and the University of Illinois Urbana-Champaign (UIUC), has developed a “photon-magnon hybrid chip” and successfully implemented real-time, multi-pulse interference using magnetic materials—marking a global first.
< Photo 1. Dr. Moojune Song (left) and Professor Kab-Jin Kim (right) of KAIST Department of Physics >
In simple terms, the researchers developed a special chip that synchronizes light and internal magnetic vibrations (magnons), enabling the transmission of phase information between distant magnets. They succeeded in observing and controlling interference between multiple signals in real time. This marks the first experimental evidence that magnets can serve as key components in quantum computing, serving as a pivotal step toward magnet-based quantum platforms.
The N and S poles of a magnet stem from the spin of electrons inside atoms. When many atoms align, their collective spin vibrations create a quantum particle known as a “magnon.”
Magnons are especially promising because of their nonreciprocal nature—they can carry information in only one direction, which makes them suitable for quantum noise isolation in compact quantum chips. They can also couple with both light and microwaves, enabling the potential for long-distance quantum communication over tens of kilometers.
Moreover, using special materials like antiferromagnets could allow quantum computers to operate at terahertz (THz) frequencies, far surpassing today’s hardware limitations, and possibly enabling room-temperature quantum computing without the need for bulky cryogenic equipment.
To build such a system, however, one must be able to transmit, measure, and control the phase information of magnons—the starting point and propagation of their waveforms—in real time. This had not been achieved until now.
< Figure 1. Superconducting Circuit-Based Magnon-Photon Hybrid System. (a) Schematic diagram of the device. A NbN superconducting resonator circuit fabricated on a silicon substrate is coupled with spherical YIG magnets (250 μm diameter), and magnons are generated and measured in real-time via a vertical antenna. (b) Photograph of the actual device. The distance between the two YIG spheres is 12 mm, a distance at which they cannot influence each other without the superconducting circuit. >
Professor Kim’s team used two tiny magnetic spheres made of Yttrium Iron Garnet (YIG) placed 12 mm apart with a superconducting resonator in between—similar to those used in quantum processors by Google and IBM. They input pulses into one magnet and successfully observed lossless transmission of magnon vibrations to the second magnet via the superconducting circuit.
They confirmed that from single nanosecond pulses to four microwave pulses, the magnon vibrations maintained their phase information and demonstrated predictable constructive or destructive interference in real time—known as coherent interference.
By adjusting the pulse frequencies and their intervals, the researchers could also freely control the interference patterns of magnons, effectively showing for the first time that electrical signals can be used to manipulate magnonic quantum states.
This work demonstrated that quantum gate operations using multiple pulses—a fundamental technique in quantum information processing—can be implemented using a hybrid system of magnetic materials and superconducting circuits. This opens the door for the practical use of magnet-based quantum devices.
< Figure 2. Experimental Data. (a) Measurement results of magnon-magnon band anticrossing via continuous wave measurement, showing the formation of a strong coupling hybrid system. (b) Magnon pulse exchange oscillation phenomenon between YIG spheres upon single pulse application. It can be seen that magnon information is coherently transmitted at regular time intervals through the superconducting circuit. (c,d) Magnon interference phenomenon upon dual pulse application. The magnon information state can be arbitrarily controlled by adjusting the time interval and carrier frequency between pulses. >
Professor Kab-Jin Kim stated, “This project began with a bold, even unconventional idea proposed to the Global Singularity Research Program: ‘What if we could build a quantum computer with magnets?’ The journey has been fascinating, and this study not only opens a new field of quantum spintronics, but also marks a turning point in developing high-efficiency quantum information processing devices.”
The research was co-led by postdoctoral researcher Moojune Song (KAIST), Dr. Yi Li and Dr. Valentine Novosad from Argonne National Lab, and Prof. Axel Hoffmann’s team at UIUC. The results were published in Nature Communications on April 17 and npj Spintronics on April 1, 2025.
Paper 1: Single-shot magnon interference in a magnon-superconducting-resonator hybrid circuit, Nat. Commun. 16, 3649 (2025)
DOI: https://doi.org/10.1038/s41467-025-58482-2
Paper 2: Single-shot electrical detection of short-wavelength magnon pulse transmission in a magnonic ultra-thin-film waveguide, npj Spintronics 3, 12 (2025)
DOI: https://doi.org/10.1038/s44306-025-00072-5
The research was supported by KAIST’s Global Singularity Research Initiative, the National Research Foundation of Korea (including the Mid-Career Researcher, Leading Research Center, and Quantum Information Science Human Resource Development programs), and the U.S. Department of Energy.
2025.06.12 View 825 -
KAIST Succeeds in Real-Time Carbon Dioxide Monitoring Without Batteries or External Power
< (From left) Master's Student Gyurim Jang, Professor Kyeongha Kwon >
KAIST (President Kwang Hyung Lee) announced on June 9th that a research team led by Professor Kyeongha Kwon from the School of Electrical Engineering, in a joint study with Professor Hanjun Ryu's team at Chung-Ang University, has developed a self-powered wireless carbon dioxide (CO2) monitoring system. This innovative system harvests fine vibrational energy from its surroundings to periodically measure CO2 concentrations.
This breakthrough addresses a critical need in environmental monitoring: accurately understanding "how much" CO2 is being emitted to combat climate change and global warming. While CO2 monitoring technology is key to this, existing systems largely rely on batteries or wired power system, imposing limitations on installation and maintenance. The KAIST team tackled this by creating a self-powered wireless system that operates without external power.
The core of this new system is an "Inertia-driven Triboelectric Nanogenerator (TENG)" that converts vibrations (with amplitudes ranging from 20-4000 ㎛ and frequencies from 0-300 Hz) generated by industrial equipment or pipelines into electricity. This enables periodic CO2 concentration measurements and wireless transmission without the need for batteries.
< Figure 1. Concept and configuration of self-powered wireless CO2 monitoring system using fine vibration harvesting (a) System block diagram (b) Photo of fabricated system prototype >
The research team successfully amplified fine vibrations and induced resonance by combining spring-attached 4-stack TENGs. They achieved stable power production of 0.5 mW under conditions of 13 Hz and 0.56 g acceleration. The generated power was then used to operate a CO2 sensor and a Bluetooth Low Energy (BLE) system-on-a-chip (SoC).
Professor Kyeongha Kwon emphasized, "For efficient environmental monitoring, a system that can operate continuously without power limitations is essential." She explained, "In this research, we implemented a self-powered system that can periodically measure and wirelessly transmit CO2 concentrations based on the energy generated from an inertia-driven TENG." She added, "This technology can serve as a foundational technology for future self-powered environmental monitoring platforms integrating various sensors."
< Figure 2. TENG energy harvesting-based wireless CO2 sensing system operation results (c) Experimental setup (d) Measured CO2 concentration results powered by TENG and conventional DC power source >
This research was published on June 1st in the internationally renowned academic journal `Nano Energy (IF 16.8)`. Gyurim Jang, a master's student at KAIST, and Daniel Manaye Tiruneh, a master's student at Chung-Ang University, are the co-first authors of the paper.*Paper Title: Highly compact inertia-driven triboelectric nanogenerator for self-powered wireless CO2 monitoring via fine-vibration harvesting*DOI: 10.1016/j.nanoen.2025.110872
This research was supported by the Saudi Aramco-KAIST CO2 Management Center.
2025.06.09 View 44499 -
KAIST Research Team Develops Electronic Ink for Room-Temperature Printing of High-Resolution, Variable-Stiffness Electronics
A team of researchers from KAIST and Seoul National University has developed a groundbreaking electronic ink that enables room-temperature printing of variable-stiffness circuits capable of switching between rigid and soft modes. This advancement marks a significant leap toward next-generation wearable, implantable, and robotic devices.
< Photo 1. (From left) Professor Jae-Woong Jeong and PhD candidate Simok Lee of the School of Electrical Engineering, (in separate bubbles, from left) Professor Gun-Hee Lee of Pusan National University, Professor Seongjun Park of Seoul National University, Professor Steve Park of the Department of Materials Science and Engineering>
Variable-stiffness electronics are at the forefront of adaptive technology, offering the ability for a single device to transition between rigid and soft modes depending on its use case. Gallium, a metal known for its high rigidity contrast between solid and liquid states, is a promising candidate for such applications. However, its use has been hindered by challenges including high surface tension, low viscosity, and undesirable phase transitions during manufacturing.
On June 4th, a research team led by Professor Jae-Woong Jeong from the School of Electrical Engineering at KAIST, Professor Seongjun Park from the Digital Healthcare Major at Seoul National University, and Professor Steve Park from the Department of Materials Science and Engineering at KAIST introduced a novel liquid metal electronic ink. This ink allows for micro-scale circuit printing – thinner than a human hair – at room temperature, with the ability to reversibly switch between rigid and soft modes depending on temperature.
The new ink combines printable viscosity with excellent electrical conductivity, enabling the creation of complex, high-resolution multilayer circuits comparable to commercial printed circuit boards (PCBs). These circuits can dynamically change stiffness in response to temperature, presenting new opportunities for multifunctional electronics, medical technologies, and robotics.
Conventional electronics typically have fixed form factors – either rigid for durability or soft for wearability. Rigid devices like smartphones and laptops offer robust performance but are uncomfortable when worn, while soft electronics are more comfortable but lack precise handling. As demand grows for devices that can adapt their stiffness to context, variable-stiffness electronics are becoming increasingly important.
< Figure 1. Fabrication process of stable, high-viscosity electronic ink by dispersing micro-sized gallium particles in a polymer matrix (left). High-resolution large-area circuit printing process through pH-controlled chemical sintering (right). >
To address this challenge, the researchers focused on gallium, which melts just below body temperature. Solid gallium is quite stiff, while its liquid form is fluid and soft. Despite its potential, gallium’s use in electronic printing has been limited by its high surface tension and instability when melted.
To overcome these issues, the team developed a pH-controlled liquid metal ink printing process. By dispersing micro-sized gallium particles into a hydrophilic polyurethane matrix using a neutral solvent (dimethyl sulfoxide, or DMSO), they created a stable, high-viscosity ink suitable for precision printing. During post-print heating, the DMSO decomposes to form an acidic environment, which removes the oxide layer on the gallium particles. This triggers the particles to coalesce into electrically conductive networks with tunable mechanical properties.
The resulting printed circuits exhibit fine feature sizes (~50 μm), high conductivity (2.27 × 10⁶ S/m), and a stiffness modulation ratio of up to 1,465 – allowing the material to shift from plastic-like rigidity to rubber-like softness. Furthermore, the ink is compatible with conventional printing techniques such as screen printing and dip coating, supporting large-area and 3D device fabrication.
< Figure 2. Key features of the electronic ink. (i) High-resolution printing and multilayer integration capability. (ii) Batch fabrication capability through large-area screen printing. (iii) Complex three-dimensional structure printing capability through dip coating. (iv) Excellent electrical conductivity and stiffness control capability.>
The team demonstrated this technology by developing a multi-functional device that operates as a rigid portable electronic under normal conditions but transforms into a soft wearable healthcare device when attached to the body. They also created a neural probe that remains stiff during surgical insertion for accurate positioning but softens once inside brain tissue to reduce inflammation – highlighting its potential for biomedical implants.
< Figure 3. Variable stiffness wearable electronics with high-resolution circuits and multilayer structure comparable to commercial printed circuit boards (PCBs). Functions as a rigid portable electronic device at room temperature, then transforms into a wearable healthcare device by softening at body temperature upon skin contact.>
“The core achievement of this research lies in overcoming the longstanding challenges of liquid metal printing through our innovative technology,” said Professor Jeong. “By controlling the ink’s acidity, we were able to electrically and mechanically connect printed gallium particles, enabling the room-temperature fabrication of high-resolution, large-area circuits with tunable stiffness. This opens up new possibilities for future personal electronics, medical devices, and robotics.”
< Figure 4. Body-temperature softening neural probe implemented by coating electronic ink on an optical waveguide structure. (Left) Remains rigid during surgery for precise manipulation and brain insertion, then softens after implantation to minimize mechanical stress on the brain and greatly enhance biocompatibility. (Right) >
This research was published in Science Advances under the title, “Phase-Change Metal Ink with pH-Controlled Chemical Sintering for Versatile and Scalable Fabrication of Variable Stiffness Electronics.” The work was supported by the National Research Foundation of Korea, the Boston-Korea Project, and the BK21 FOUR Program.
2025.06.04 View 1106 -
Professor Hyun Myung's Team Wins First Place in a Challenge at ICRA by IEEE
< Photo 1. (From left) Daebeom Kim (Team Leader, Ph.D. student), Seungjae Lee (Ph.D. student), Seoyeon Jang (Ph.D. student), Jei Kong (Master's student), Professor Hyun Myung >
A team of the Urban Robotics Lab, led by Professor Hyun Myung from the KAIST School of Electrical Engineering, achieved a remarkable first-place overall victory in the Nothing Stands Still Challenge (NSS Challenge) 2025, held at the 2025 IEEE International Conference on Robotics and Automation (ICRA), the world's most prestigious robotics conference, from May 19 to 23 in Atlanta, USA.
The NSS Challenge was co-hosted by HILTI, a global construction company based in Liechtenstein, and Stanford University's Gradient Spaces Group. It is an expanded version of the HILTI SLAM (Simultaneous Localization and Mapping)* Challenge, which has been held since 2021, and is considered one of the most prominent challenges at 2025 IEEE ICRA.*SLAM: Refers to Simultaneous Localization and Mapping, a technology where robots, drones, autonomous vehicles, etc., determine their own position and simultaneously create a map of their surroundings.
< Photo 2. A scene from the oral presentation on the winning team's technology (Speakers: Seungjae Lee and Seoyeon Jang, Ph.D. candidates of KAIST School of Electrical Engineering) >
This challenge primarily evaluates how accurately and robustly LiDAR scan data, collected at various times, can be registered in situations with frequent structural changes, such as construction and industrial environments. In particular, it is regarded as a highly technical competition because it deals with multi-session localization and mapping (Multi-session SLAM) technology that responds to structural changes occurring over multiple timeframes, rather than just single-point registration accuracy.
The Urban Robotics Lab team secured first place overall, surpassing National Taiwan University (3rd place) and Northwestern Polytechnical University of China (2nd place) by a significant margin, with their unique localization and mapping technology that solves the problem of registering LiDAR data collected across multiple times and spaces. The winning team will be awarded a prize of $4,000.
< Figure 1. Example of Multiway-Registration for Registering Multiple Scans >
The Urban Robotics Lab team independently developed a multiway-registration framework that can robustly register multiple scans even without prior connection information. This framework consists of an algorithm for summarizing feature points within scans and finding correspondences (CubicFeat), an algorithm for performing global registration based on the found correspondences (Quatro), and an algorithm for refining results based on change detection (Chamelion). This combination of technologies ensures stable registration performance based on fixed structures, even in highly dynamic industrial environments.
< Figure 2. Example of Change Detection Using the Chamelion Algorithm>
LiDAR scan registration technology is a core component of SLAM (Simultaneous Localization And Mapping) in various autonomous systems such as autonomous vehicles, autonomous robots, autonomous walking systems, and autonomous flying vehicles.
Professor Hyun Myung of the School of Electrical Engineering stated, "This award-winning technology is evaluated as a case that simultaneously proves both academic value and industrial applicability by maximizing the performance of precisely estimating the relative positions between different scans even in complex environments. I am grateful to the students who challenged themselves and never gave up, even when many teams abandoned due to the high difficulty."
< Figure 3. Competition Result Board, Lower RMSE (Root Mean Squared Error) Indicates Higher Score (Unit: meters)>
The Urban Robotics Lab team first participated in the SLAM Challenge in 2022, winning second place among academic teams, and in 2023, they secured first place overall in the LiDAR category and first place among academic teams in the vision category.
2025.05.30 View 1230 -
KAIST Develops Virtual Staining Technology for 3D Histopathology
Moving beyond traditional methods of observing thinly sliced and stained cancer tissues, a collaborative international research team led by KAIST has successfully developed a groundbreaking technology. This innovation uses advanced optical techniques combined with an artificial intelligence-based deep learning algorithm to create realistic, virtually stained 3D images of cancer tissue without the need for serial sectioning nor staining. This breakthrough is anticipated to pave the way for next-generation non-invasive pathological diagnosis.
< Photo 1. (From left) Juyeon Park (Ph.D. Candidate, Department of Physics), Professor YongKeun Park (Department of Physics) (Top left) Professor Su-Jin Shin (Gangnam Severance Hospital), Professor Tae Hyun Hwang (Vanderbilt University School of Medicine) >
KAIST (President Kwang Hyung Lee) announced on the 26th that a research team led by Professor YongKeun Park of the Department of Physics, in collaboration with Professor Su-Jin Shin's team at Yonsei University Gangnam Severance Hospital, Professor Tae Hyun Hwang's team at Mayo Clinic, and Tomocube's AI research team, has developed an innovative technology capable of vividly displaying the 3D structure of cancer tissues without separate staining.
For over 200 years, conventional pathology has relied on observing cancer tissues under a microscope, a method that only shows specific cross-sections of the 3D cancer tissue. This has limited the ability to understand the three-dimensional connections and spatial arrangements between cells.
To overcome this, the research team utilized holotomography (HT), an advanced optical technology, to measure the 3D refractive index information of tissues. They then integrated an AI-based deep learning algorithm to successfully generate virtual H&E* images.* H&E (Hematoxylin & Eosin): The most widely used staining method for observing pathological tissues. Hematoxylin stains cell nuclei blue, and eosin stains cytoplasm pink.
The research team quantitatively demonstrated that the images generated by this technology are highly similar to actual stained tissue images. Furthermore, the technology exhibited consistent performance across various organs and tissues, proving its versatility and reliability as a next-generation pathological analysis tool.
< Figure 1. Comparison of conventional 3D tissue pathology procedure and the 3D virtual H&E staining technology proposed in this study. The traditional method requires preparing and staining dozens of tissue slides, while the proposed technology can reduce the number of slides by up to 10 times and quickly generate H&E images without the staining process. >
Moreover, by validating the feasibility of this technology through joint research with hospitals and research institutions in Korea and the United States, utilizing Tomocube's holotomography equipment, the team demonstrated its potential for full-scale adoption in real-world pathological research settings.
Professor YongKeun Park stated, "This research marks a major advancement by transitioning pathological analysis from conventional 2D methods to comprehensive 3D imaging. It will greatly enhance biomedical research and clinical diagnostics, particularly in understanding cancer tumor boundaries and the intricate spatial arrangements of cells within tumor microenvironments."
< Figure 2. Results of AI-based 3D virtual H&E staining and quantitative analysis of pathological tissue. The virtually stained images enabled 3D reconstruction of key pathological features such as cell nuclei and glandular lumens. Based on this, various quantitative indicators, including cell nuclear distribution, volume, and surface area, could be extracted. >
This research, with Juyeon Park, a student of the Integrated Master’s and Ph.D. Program at KAIST, as the first author, was published online in the prestigious journal Nature Communications on May 22.
(Paper title: Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.
[https://doi.org/10.1038/s41467-025-59820-0]
This study was supported by the Leader Researcher Program of the National Research Foundation of Korea, the Global Industry Technology Cooperation Center Project of the Korea Institute for Advancement of Technology, and the Korea Health Industry Development Institute.
2025.05.26 View 1739 -
KAIST to Develop a Korean-style ChatGPT Platform Specifically Geared Toward Medical Diagnosis and Drug Discovery
On May 23rd, KAIST (President Kwang-Hyung Lee) announced that its Digital Bio-Health AI Research Center (Director: Professor JongChul Ye of KAIST Kim Jaechul Graduate School of AI) has been selected for the Ministry of Science and ICT's 'AI Top-Tier Young Researcher Support Program (AI Star Fellowship Project).' With a total investment of ₩11.5 billion from May 2025 to December 2030, the center will embark on the full-scale development of AI technology and a platform capable of independently inferring and determining the kinds of diseases, and discovering new drugs.
< Photo. On May 20th, a kick-off meeting for the AI Star Fellowship Project was held at KAIST Kim Jaechul Graduate School of AI’s Yangjae Research Center with the KAIST research team and participating organizations of Samsung Medical Center, NAVER Cloud, and HITS. [From left to right in the front row] Professor Jaegul Joo (KAIST), Professor Yoonjae Choi (KAIST), Professor Woo Youn Kim (KAIST/HITS), Professor JongChul Ye (KAIST), Professor Sungsoo Ahn (KAIST), Dr. Haanju Yoo (NAVER Cloud), Yoonho Lee (KAIST), HyeYoon Moon (Samsung Medical Center), Dr. Su Min Kim (Samsung Medical Center) >
This project aims to foster an innovative AI research ecosystem centered on young researchers and develop an inferential AI agent that can utilize and automatically expand specialized knowledge systems in the bio and medical fields.
Professor JongChul Ye of the Kim Jaechul Graduate School of AI will serve as the lead researcher, with young researchers from KAIST including Professors Yoonjae Choi, Kimin Lee, Sungsoo Ahn, and Chanyoung Park, along with mid-career researchers like Professors Jaegul Joo and Woo Youn Kim, jointly undertaking the project. They will collaborate with various laboratories within KAIST to conduct comprehensive research covering the entire cycle from the theoretical foundations of AI inference to its practical application.
Specifically, the main goals include: - Building high-performance inference models that integrate diverse medical knowledge systems to enhance the precision and reliability of diagnosis and treatment. - Developing a convergence inference platform that efficiently combines symbol-based inference with neural network models. - Securing AI technology for new drug development and biomarker discovery based on 'cell ontology.'
Furthermore, through close collaboration with industry and medical institutions such as Samsung Medical Center, NAVER Cloud, and HITS Co., Ltd., the project aims to achieve: - Clinical diagnostic AI utilizing medical knowledge systems. - AI-based molecular target exploration for new drug development. - Commercialization of an extendible AI inference platform.
Professor JongChul Ye, Director of KAIST's Digital Bio-Health AI Research Center, stated, "At a time when competition in AI inference model development is intensifying, it is a great honor for KAIST to lead the development of AI technology specialized in the bio and medical fields with world-class young researchers." He added, "We will do our best to ensure that the participating young researchers reach a world-leading level in terms of research achievements after the completion of this seven-year project starting in 2025."
The AI Star Fellowship is a newly established program where post-doctoral researchers and faculty members within seven years of appointment participate as project leaders (PLs) to independently lead research. Multiple laboratories within a university and demand-side companies form a consortium to operate the program.
Through this initiative, KAIST plans to nurture bio-medical convergence AI talent and simultaneously promote the commercialization of core technologies in collaboration with Samsung Medical Center, NAVER Cloud, and HITS.
2025.05.26 View 2018 -
KAIST and Mainz Researchers Unveil 3D Magnon Control, Charting a New Course for Neuromorphic and Quantum Technologies
< Professor Se Kwon Kim of the Department of Physics (left), Dr. Zarzuela of the University of Mainz, Germany (right) >
What if the magnon Hall effect, which processes information using magnons (spin waves) capable of current-free information transfer with magnets, could overcome its current limitation of being possible only on a 2D plane? If magnons could be utilized in 3D space, they would enable flexible design, including 3D circuits, and be applicable in various fields such as next-generation neuromorphic (brain-mimicking) computing structures, similar to human brain information processing. KAIST and an international joint research team have, for the first time in the world, predicted a 3D magnon Hall effect, demonstrating that magnons can move freely and complexly in 3D space, transcending the conventional concept of magnons.
KAIST (President Kwang Hyung Lee) announced on May 22nd that Professor Se Kwon Kim of the Department of Physics, in collaboration with Dr. Ricardo Zarzuela of the University of Mainz, Germany, has revealed that the interaction between magnons (spin waves) and solitons (spin vortices) within complex magnetic structures (topologically textured frustrated magnets) is not simple, but complex in a way that enables novel functionalities.
Magnons (spin waves), which can transmit information like electron movement, are garnering attention as a next-generation information processing technology that transmits information without using current, thus generating no heat. Until now, magnon research has focused on simple magnets where spins are neatly aligned in one direction, and the mathematics describing this was a relatively simple 'Abelian gauge theory.'
The research team demonstrated, for the first time in the world, that in complex spin structures like frustrated magnets, magnons interact and become entangled in complex ways from various directions. They applied an advanced mathematical framework, 'non-Abelian gauge theory,' to describe this movement, which is a groundbreaking achievement.
This research presents the possibility of future applications in low-power logic devices using magnons and topology-based quantum information processing technologies, indicating a potential paradigm shift in future information technology.
In conventional linear magnetic materials, the value representing the magnetic state (order parameter) is given as a vector. In magnonics research based on this, it has been interpreted that a U(1) Abelian gauge field is induced when magnons move in soliton structures like skyrmions. This means that the interaction between solitons and magnons has a structure similar to quantum electrodynamics (QED), which has successfully explained various experimental results such as the magnon Hall effect in 2D magnets.
< Figure. Schematic diagram of non-Abelian magnon quantum chromodynamics describing the dynamics of three types of magnons discovered for the first time in this study.>
However, through this research, the team theoretically revealed that in frustrated magnets, the order parameter must be expressed not as a simple vector but as a quaternion. As a result, the gauge field experienced by magnons resembles an SU(3) non-Abelian gauge field, rather than a simple U(1) Abelian gauge field.
This implies that within frustrated magnets, there are not one or two types of magnons seen in conventional magnets, but three distinct types of magnons, each interacting and intricately entangled with solitons. This structure is highly significant as it resembles quantum chromodynamics (QCD) that describes the strong interaction between quarks mediated by gluons rather than quantum electrodynamics (QED) that describes electromagnetic forces.
Professor Se Kwon Kim stated, "This research presents a powerful theoretical framework to explain the dynamics of magnons occurring within the complex order of frustrated magnets," adding, "By pioneering non-Abelian magnonics, it will be a conceptual turning point that can influence quantum magnetism research as a whole."
The research results, with Dr. Ricardo Zarzuela of the University of Mainz, Germany, as the first author, were published in the world-renowned physics journal Physical Review Letters on May 6th.※ Paper title: "Non-Abelian Gauge Theory for Magnons in Topologically Textured Frustrated Magnets," Phys. Rev. Lett. 134, 186701 (2025)DOI: https://doi.org/10.1103/PhysRevLett.134.186701
This research was supported by the Brain Pool Plus program of the National Research Foundation of Korea.
2025.05.22 View 1762