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The World's Smallest Fully Wireless Neural Implant Achieved
< (From left) Sunwoo Lee, KAIST Joint Professor, Alyosha Molnar, Cornell University Professor >
The human brain contains about 100 billion brain cells, and the chemical and electrical signals they exchange create most mental functions. Neural implant technology for precisely reading these signals is essential for the research and treatment of neurodegenerative diseases. A research team from KAIST and international collaborators has successfully implemented a fully wireless, ultra-small implant, which was previously only a theoretical possibility, going beyond simple miniaturization and weight reduction of neural implants.
KAIST announced on the November 27th that a joint research team led by Professor Sunwoo Lee (Joint Professor in Materials Science and Engineering at KAIST and from the School of Electrical and Electronic Engineering at Nanyang Technological University, NTU) and Professor Alyosha Molnar's team from Cornell University in the US has developed 'MOTE (Micro-Scale Opto-Electronic Tetherless Electrode)', an ultra-small wireless neural implant less than 100 micrometers (µm) — smaller than a grain of salt. The team successfully implanted this device into the brains of laboratory mice and stably measured brain waves for one year.
In the brain, invisible, minute electrical signals constantly move, creating our various mental activities such as memory, judgment, and emotion. The technology to directly measure these signals outside the body without connecting wires has been highlighted as key for brain research and the treatment of neurological disorders like dementia and Parkinson's disease.
However, existing implants have limitations: their thick wired structure causes movement in the brain, leading to inflammation and signal degradation over time, and their size and heat generation restrict long-term use.
To overcome these limitations, the research team created an ultra-small circuit based on the existing semiconductor process (CMOS) and combined it with their self-developed ultra-fine Micro-LEDs (µLEDs) to drastically miniaturize the device. They also applied a special surface coating to significantly enhance durability, allowing it to withstand the biological environment for a long time.
The resulting MOTE is less than 100 µm thick and has a volume of less than 1 nanoliter, making it thinner than a human hair and smaller than a grain of salt, the world's smallest level among currently reported wireless neural implants.
Another key feature of MOTE is that it is a fully wireless system that requires no battery. The device is structured to receive external light to generate power, detect brain waves, and then transmit the information back outside embedded in the light signal using Pulse Position Modulation (PPM).
This method drastically reduces energy consumption, minimizes the risk of heat generation, and eliminates the need for battery replacement, enabling long-term use.
The research team conducted a one-year long-term experiment by implanting the ultra-small MOTE into the brains of mice. The results showed normal brain wave measurement over the extended period, with almost no inflammation observed around the implant and no degradation in device performance.
This is considered the first clear demonstration that an ultra-small wireless implant can maintain normal function for a prolonged time inside a living body.
< MOTE neural implant on a salt crystal (left), MOTE neural implants after 296 days of implantation in a laboratory mouse (right) >
Professor Sunwoo Lee stated, "The greatest significance of the newly developed neural implant lies in its actual implementation of a fully wireless, ultra-small implant that was previously only anticipated as a possibility, going beyond simple miniaturization and weight reduction." He added, "This proves the technological possibility of resolving not only the known unknowns raised during the development and use of wireless neural implants, but also the unknown unknowns that newly emerge during the actual development process."
He further added, "This technology will be broadly applicable not only to brain science research but also to nervous system disease monitoring and the development of long-term recording-based treatment technologies."
The research results were published online in the prestigious journal Nature Electronics on November 3rd. ※ Paper Title: A subnanolitre tetherless optoelectronic microsystem for chronic neural recording in awake mice, DOI: https://doi.org/10.1038/s41928-025-01484-1
This research was supported by the US National Institutes of Health (NIH), Nanyang Technological University (Singapore), the Singapore National Research Foundation, the Singapore Ministry of Education, and the ASPIRE League Partnership Seed Fund 2024. The specialized fabrication processes were conducted at the Cornell NanoScale Facility (part of the US National Nanotechnology Coordinated Infrastructure, NNCI) and NTU's Nanyang NanoFabrication Centre.
2025.11.27 View 411 -
“AI,” the New Language of Materials Science and Engineering Spoken at KAIST
<(From Left) M.S candidate Chaeyul Kang, Professor Seumgbum Hong, Ph. D candidate Benediktus Madika, Ph.D candidate Batzorig Buyantogtokh, Ph.D candiate Aditi Saha, >
Collaborating authors include Professor Joshua Agar (Drexel University), Professors Chris Wolverton and Peter Voorhees (Northwestern University), Professor Peter Littlewood (University of St Andrews), and Professor Sergei Kalinin (University of Tennessee).
Paper Title: Artificial Intelligence for Materials Discovery, Development, and Optimization
The era has arrived in which artificial intelligence (AI) autonomously imagines and predicts the structures and properties of new materials. Today, AI functions as a researcher’s “second brain,” actively participating in every stage of research, from idea generation to experimental validation.
KAIST (President Kwang Hyung Lee) announced on October 26 that a comprehensive review paper analyzing the impact of AI, Machine Learning (ML), and Deep Learning (DL) technologies across materials science and engineering has been published in ACS Nano (Impact Factor = 18.7). The paper was co-authored by Professor Seungbum Hong and his team from the Department of Materials Science and Engineering at KAIST, in collaboration with researchers from Drexel University, Northwestern University, the University of St Andrews, and the University of Tennessee in the United States.
The research team proposed a full-cycle utilization strategy for materials innovation through an AI-based catalyst search platform, which embodies the concept of a Self-Driving Lab—a system in which robots autonomously perform materials synthesis and optimization experiments.
Professor Hong’s team categorized materials research into three major stages—Discovery, Development, and Optimization—and detailed the distinctive role of AI in each phase:
In the Discovery Stage, AI designs new structures, predicts properties, and rapidly identifies the most promising materials among vast candidate pools.
In the Development Stage, AI analyzes experimental data and autonomously adjusts experimental processes through Self-Driving Lab systems, significantly shortening research timelines.
In the Optimization Stage, AI employs Reinforcement Learning, which identifies optimal conditions through Bayesian Optimization, which efficiently finds superior results with minimal experimentation, to fine-tune designs and process conditions for maximum performance.
In essence, AI serves as a “smart assistant” that narrows down the most promising materials, reduces experimental trial and error, and autonomously optimizes experimental conditions to achieve the best-performing outcomes.
The paper further highlights how cutting-edge technologies such as Generative AI, Graph Neural Networks (GNNs), and Transformer models are transforming AI from a computational tool into a “thinking researcher.” Nonetheless, the team cautions that AI’s predictions are not error-proof and that key challenges persist, such as imbalanced data quality, limited interpretability of AI predictions, and integration of heterogeneous datasets.
To address these limitations, the authors emphasize the importance of developing AI systems capable of autonomously understanding physical principles and ensuring transparent, verifiable decision-making processes for researchers.
The review also explores the concept of the Self-Driving Lab, where AI autonomously designs experimental plans, analyzes results, and determines the next experimental steps—without manual operation by researchers. The AI-Based Catalyst Search Platform exemplifies this concept, enabling robots to automatically design, execute, and optimize catalyst synthesis experiments.
In particular, the study presents cases in which AI-driven experimentation has dramatically accelerated catalyst development, suggesting that similar approaches could revolutionize research in battery and energy materials.
<AI Driving Innovation Across the Entire Cycle of New Material Discovery, Development, and Optimization>
“This review demonstrates that artificial intelligence is emerging as the new language of materials science and engineering, transcending its role as a mere tool,” said Professor Seungbum Hong. “The roadmap presented by the KAIST team will serve as a valuable guide for researchers in Korea’s national core industries including batteries, semiconductors, and energy materials.”
Benediktus Madika (Ph.D. candidate), Aditi Saha (Ph.D. candidate), Chaeyul Kang (M.S. candidate), and Batzorig Buyantogtokh (Ph.D. candidate) from KAIST’s Department of Materials Science and Engineering contributed as co-first authors.
Collaborating authors include Professor Joshua Agar (Drexel University), Professors Chris Wolverton and Peter Voorhees (Northwestern University), Professor Peter Littlewood (University of St Andrews), and Professor Sergei Kalinin (University of Tennessee).
Paper Title: Artificial Intelligence for Materials Discovery, Development, and Optimization
DOI: 10.1021/acsnano.5c04200
This work was supported by the National Research Foundation of Korea (NRF) with funding from the Ministry of Science and ICT (RS-2023-00247245).
2025.10.27 View 909 -
Physics Informed AI Excels at Large Scale Discovery of New Materials!
<(From left) Ph.D candidates Songho Lee, Donggeun Park, and Hyeonbin Moon, and Professor Seunghwa Ryu from the Department of Mechanical Engineering; (top) Professor Jae Hyuk Lim from Kyung Hee University and Dr. Wabi Demeke from KAIST>
One of the key steps in developing new materials is “property identification,” which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A KAIST research team has introduced a new technique that combines “physical laws,” which govern deformation and interaction of materials and energy, with artificial intelligence. This approach allows for rapid exploration of new materials even under data-scarce conditions and provides a foundation for accelerating design and verification across multiple engineering fields, including materials, mechanics, energy, and electronics.
KAIST (President Kwang Hyung Lee) announced on the 2nd of October that Professor Seunghwa Ryu’s research group in the Department of Mechanical Engineering, in collaboration with Professor Jae Hyuk Lim’s group at Kyung Hee University (President Jinsang Kim) and Dr. Byungki Ryu at the Korea Electrotechnology Research Institute (President Namkyun Kim), proposed a new method that can accurately determine material properties with only limited data. The method uses Physics-Informed Machine Learning (PIML), which directly incorporates physical laws into the AI learning process.
<Schematic Diagram of a Physics-Based Machine Learning Methodology for Understanding Material Properties>
In the first study, the researchers focused on hyperelastic materials, such as rubber. They presented a Physics-Informed Neural Network (PINN) method that can identify both the deformation behavior and the properties of materials using only a small amount of data obtained from a single experiment. Whereas previous approaches required large, complex datasets, this research demonstrated that material characteristics can be reliably reproduced even when data is scarce, limited, or noisy.
In the second study, the team turned to thermoelectric materials—new materials that convert heat into electricity and electricity into heat. They proposed a PINN-based inverse inference technique that can estimate key indicators, such as thermal conductivity (how well heat is transferred) and the Seebeck coefficient (how efficiently electricity is generated), from just a few measurements.
Going further, the researchers introduced a Physics-Informed Neural Operator (PINO), an AI model that understands the physical laws of nature, and showed that it can generalize to previously unseen materials without requiring retraining.
In fact, after training the system on 20 materials, they tested it on 60 entirely new materials, and in all cases it predicted their properties with high accuracy. This breakthrough points to a future where large-scale, high-speed screening of countless candidate materials becomes possible.
This achievement goes beyond simply reducing the need for experiments. By intricately combining physical laws with AI, the researchers provided the first example of improving experimental efficiency while preserving reliability.
Professor Seunghwa Ryu, who led both studies, stated, “This is the first case of applying AI that understands physical laws to real material research. It enables reliable identification of material properties even when data availability is limited, and it is expected to expand into various engineering fields.”
The first paper, co-first-authored by KAIST Mechanical Engineering PhD candidates Hyeonbin Moon and Donggeun Park, was published on August 13 in Computer Methods in Applied Mechanics and Engineering.
※ Paper title: “Physics-informed neural network-based discovery of hyperelastic constitutive models from extremely scarce data”
※ DOI: https://doi.org/10.1016/j.cma.2025.118258
The second paper, co-first-authored by KAIST Mechanical Engineering PhD candidates Hyeonbin Moon and Songho Lee, and Dr. Wabi Demeke, was published on August 22 in npj Computational Materials.
※ Paper title: “Physics-informed neural operators for generalizable and label-free inference of temperature-dependent thermoelectric properties” ※ DOI: https://doi.org/10.1038/s41524-025-01769-1
Meanwhile, the first study was supported by the Korea Research Foundation and the Ministry of Science and ICT’s INNOCore Program, as well as by a research project from the Ministry of Food and Drug Safety. The second study was carried out with support from the Korea Research Foundation and the Ministry of Science and ICT’s INNOCore Program.
2025.10.10 View 900 -
Next Generation Robots Roaming Shipyards and City Centers
< Diden Robotics Research Team Co., Ltd (Leftmost person in the front row is CEO Joon-Ha Kim)>
KAIST announced on the September 30th that domestic robot startups, founded on KAIST research achievements, are driving new innovation at shipyards and urban worksites.
An industrial walking robot that freely climbs walls and ceilings and a humanoid walking robot that walks through downtown Gangnam are attracting attention as they enter the stage of commercialization. The stars are DIDEN Robotics Co., Ltd. and Eurobotics Co., Ltd.
Diden Robotics is providing a new breakthrough in the industrial automation market, including the shipbuilding industry, by commercializing its innovative 'Seungwol (Ascend and Cross) Robot' technology, which allows it to move freely and work on steel walls and ceilings. Eurobotics is commercializing world-class humanoid walking technology, and this achievement is scheduled to be officially presented at the international humanoid robot conference, 'Humanoids 2025,' to be held on October 1st.
< Diden Robot's Outer Plate (Longi) and Welding Test >
Diden Robotics is a robotics startup jointly founded in March 2024 by four alumni from the KAIST Mechanical Engineering Hu-bo Lab DRCD research team (Professor Hae-Won Park). Its flagship product, 'DIDEN 30,' is a quadrupedal robot designed for use in high-risk work environments that are difficult for humans to access, combining autonomous driving technology, a foot-shaped leg structure, and magnetic feet.
The 'DIDEN 30' successfully completed the 'Longitudinal (longi) Overcoming Test,' in which it stepped over steel stiffeners (longitudinals) densely installed as part of the structure at a ship construction site, proving its potential for field deployment. Currently, the company is conducting research to enhance its functionality so it can stably pass through access holes, the narrow entryways inside ships. It is also pushing for performance improvements so it can be deployed for real tasks such as welding, inspection, and painting starting in the second half of 2026.
A next-generation bipedal walking robot, 'DIDEN Walker,' is also under development. Targeting the completion of a prototype in the fourth quarter of 2025, it is being designed for stable walking in cramped and complex industrial environments. Plans are also underway to equip it with an upper-body manipulator for automated welding in the shipbuilding industry.
Diden Robotics is accelerating the advancement of its proprietary 'Physical AI' technology. The core is the self-developed AI learning platform, 'DIDEN World,' which applies an offline reinforcement learning method where the AI generates optimal motion data in a virtual simulation beforehand and learns without trial and error, increasing learning efficiency and stability.
< Diden Robot (DIDEN 30) >
Furthermore, to actually implement the AI technology, the company is internalizing its hardware and advancing its 3D recognition technology, which serves as the robot's 'eyes.' It is aiming for a completely autonomous walking system that requires no worker intervention by 2026, using technology such as 3D mapping based on four cameras.
In addition to this technological development, Diden Robotics successfully performed the longitudinal overcoming, Seungwol test, and welding work on blocks under construction through a joint development with Samsung Heavy Industries in September. This is a significant achievement, meaning Diden Robotics' technology has been validated in actual industrial settings, moving beyond the laboratory level.
Meanwhile, Diden Robotics is collaborating with major domestic shipyards, including Samsung Heavy Industries, HD Hyundai Samho, Hanwha Ocean, and HD Korea Shipbuilding & Offshore Engineering, to develop site-customized robots.
Joon-Ha Kim, CEO of Diden Robotics, stated, "The successful tests at the Samsung Heavy Industries site proved the practicality and stability of our technology. We will establish ourselves as a leading company in solving labor shortages and driving automation in the shipbuilding industry."
< (Eurobotics Research Team Co., Ltd.)(Leftmost person in the top row is CEO Byung-ho Yoo) >
Eurobotics is an autonomous walking startup jointly founded by three alumni from Professor Hyun Myung's research team at KAIST. It is promoting the commercialization of autonomous walking technology for indoor and outdoor industrial sites, including rough terrain. In a recently released video, a humanoid equipped with control technology developed by Eurobotics attracted attention by walking naturally through the crowd in downtown Gangnam.
The core technology is the 'Blind Walking Controller.' It determines locomotion based only on internal information without external sensors like cameras or LiDAR, enabling stable walking regardless of day, night, or weather. The robot performs locomotion by 'imagining' the terrain without precise terrain modeling, demonstrating robust performance with the same controller across various environments such as sidewalks, downhill slopes, and stairs.
This technology originated from the quadrupedal walking competition at the 2023 International Conference on Robotics and Automation (ICRA), where Professor Myung's lab participated, and proved its world-class capability by winning, beating MIT by a large margin. At the time, Byungg-ho Yoo, CEO of Eurobotics, led the team, and Co-CTOs Min-ho Oh and Dong-kyu Lee directly participated in developing the core autonomous walking technology. Based on this, they continued further development tailored to the humanoid environment and have entered the commercialization stage.
< Eurobotics' Humanoid Walking >
Byung-ho Yoo, CEO of Eurobotics, emphasized, "This video is the first step toward complete humanoid autonomous walking. We will develop KAIST's research achievements into technologies that can be immediately utilized in industrial settings."
Hyeonmin Bae, Head of the KAIST Startup Center, said, "We will provide close support from the initial stages to help the on-campus robotics industry grow actively and assist them in settling down stably."
Kwang Hyung Lee, President of KAIST, stated, "This achievement is a representative case showing that KAIST's fundamental technologies are rapidly spreading to industrial fields through startups. KAIST will continue to actively support innovative entrepreneurship based on challenging research and help lead the global robotics industry."
※ https://2025humanoids.org https://www.seoulairobot.com/
2025.09.30 View 2309 -
KAIST Develops Semiconductor Neuron that Remembers and Responds Like the Brain
<(From left, clockwise) Professor Kyung Min Kim, Min-Gu Lee, Dae-Hee Kim, Dr. Han-Chan Song, Tae-Uk Ko, Moon-Gu Choi, and Eun-Young Kim>
The human brain does more than simply regulate synapses that exchange signals; individual neurons also process information through “intrinsic plasticity,” the adaptive ability to become more sensitive or less sensitive depending on context. Existing artificial intelligence semiconductors, however, have struggled to mimic this flexibility of the brain. A KAIST research team has now developed next-generation, ultra-low-power semiconductor technology that implements this ability as well, drawing significant attention.
KAIST (President Kwang Hyung Lee) announced on September 28 that a research team led by Professor Kyung Min Kim of the Department of Materials Science and Engineering developed a “Frequency Switching Neuristor” that mimics “intrinsic plasticity,” a property that allows neurons to remember past activity and autonomously adjust their response characteristics.
“Intrinsic plasticity” refers to the brain’s adaptive ability- for example, becoming less startled when hearing the same sound repeatedly, or responding more quickly to a specific stimulus after repeated training. The “Frequency Switching Neuristor” is an artificial neuron device that autonomously adjusts the frequency of its signals, much like how the brain becomes less startled by repeated stimuli or, conversely, increasingly sensitive through training.
The research team combined a “volatile Mott memristor,” which reacts momentarily before returning to its original state, with a “non-volatile memristor,” which remembers input signals for long periods of time. This enabled the implementation of a device that can freely control how often a neuron fires (its spiking frequency).
<Figure 1. Conceptual comparison between a neuron and a frequency-tunable neuristor. The intrinsic plasticity of brain neurons regulates excitability through ion channels. Similarly, the frequency-tunable neuristor uses a volatile Mott device to generate current spikes, while a non-volatile VCM device adjusts resistance states to realize comparable frequency modulation characteristics>
In this device, neuronal spike signals and memristor resistance changes influence each other, automatically adjusting responses. Put simply, it reproduces within a single semiconductor device how the brain becomes less startled by repeated sounds or more sensitive to repeated stimuli.
To verify the effectiveness of this technology, the researchers conducted simulations with a “sparse neural network.” They found that, through the neuron’s built-in memory function, the system achieved the same performance with 27.7% less energy consumption compared to conventional neural networks.
They also demonstrated excellent resilience: even if some neurons were damaged, intrinsic plasticity allowed the network to reorganize itself and restore performance. In other words, artificial intelligence using this technology consumes less electricity while maintaining performance, and it can compensate for partial circuit failures to resume normal operation.
Professor Kyung Min Kim, who led the research, stated, “This study implemented intrinsic plasticity, a core function of the brain, in a single semiconductor device, thereby advancing the energy efficiency and stability of AI hardware to a new level. This technology, which enables devices to remember their own state and adapt or recover even from damage, can serve as a key component in systems requiring long-term stability, such as edge computing and autonomous driving.”
This research was carried out with Dr. Woojoon Park (now at Forschungszentrum Jülich, Germany) and Dr. Hanchan Song (now at ETRI) as co-first authors, and the results were published online on August 18 in Advanced Materials (IF 26.8), a leading international journal in materials science.
※ Paper title: “Frequency Switching Neuristor for Realizing Intrinsic Plasticity and Enabling Robust Neuromorphic Computing,” DOI: 10.1002/adma.202502255
This research was supported by the National Research Foundation of Korea and Samsung Electronics.
2025.09.30 View 1110 -
World's First Quantum Computing for Lego-like Design of Porous Materials
<(From Left to Right)Professor Jihan Kim, Ph.D. candidate Sinyoung Kang, Ph.D. candidate Younghoon Kim from the Department of Chemical and Biomolecular Engineering>
Multivariate Porous Materials (MTV) are like a 'collection of Lego blocks,' allowing for customized design at a molecular level to freely create desired structures. Using these materials enables a wide range of applications, including energy storage and conversion, which can significantly contribute to solving environmental problems and advancing next-generation energy technologies. Our research team has, for the first time in the world, introduced quantum computing to solve the difficult problem of designing complex MTVs, opening an innovative path for the development of next-generation catalysts, separation membranes, and energy storage materials.
On September 9, Professor Jihan Kim's research team at our university's Department of Chemical and Biomolecular Engineering announced the development of a new framework that uses a quantum computer to efficiently explore the design space of millions of multivariate porous materials (hereafter, MTV).
MTV porous materials are structures formed by the combination of two or more organic ligands (linkers) and building block materials like metal clusters. They have great potential for use in the energy and environmental fields. Their diverse compositional combinations llow for the design and synthesis of new structures. Examples include gas adsorption, mixed gas separation, sensors, and catalysts.
However, as the number of components increases, the number of possible combinations grows exponentially. It has been impossible to design and predict the properties of complex MTV structures using the conventional method of checking every single structure with a classical computer.
The research team represented the complex porous structure as a 'network (graph) drawn on a map' and then converted each connection point and block type into qubits that a quantum computer can handle. They then asked the quantum computer to solve the problem: "Which blocks should be arranged at what ratio to create the most stable structure?"
<Figure1. Overall schematics of the quantum computing algorithm to generate feasible MTV porous materials. The algorithm consists of two mapping schemes (qubit mapping and topology mapping) to allocate building blocks in a given connectivity. Different configurations go through a predetermined Hamiltonian, which is comprised of a ratio term, occupancy term, and balance term, to capture the most feasible MTV porous material>
Because quantum computers can calculate multiple possibilities simultaneously, it's like spreading out millions of Lego houses at once and quickly picking out the sturdiest one. This allows them to explore a vast number of possibilities—which a classical computer would have to calculate one by one—with far fewer resources.
The research team also conducted experiments on four different MTV structures that have been previously reported. The results from the simulation and the IBM quantum computer were identical, demonstrating that the method "actually works well."
<Figure2. VQE sampling results for experimental structures and the structures that reproduce them, using IBM Qiskit's classical simulator. The experimental structure is predicted to be the most probable outcome of the VQE algorithm's calculation, meaning it will be generated as the most stable form of the structure.>
In the future, the team plans to combine this method with machine learning to expand it into a platform that considers not only simple structural design but also synthesis feasibility, gas adsorption performance, and electrochemical properties simultaneously.
Professor Jihan Kim said, "This research is the first case to solve the bottleneck of complex multivariate porous material design using quantum computing." He added, "This achievement is expected to be widely applied as a customized material design technology in fields where precise composition is key, such as carbon capture and separation, selective catalytic reactions, and ion-conducting electrolytes, and it can be flexibly expanded to even more complex systems in the future."
Ph.D. candidates Sinyoung Kang and Younghoon Kim of the Department of Chemical and Biomolecular Engineering participated as co-first authors in this study. The research results were published in the online edition of the international journal ACS Central Science on August 22.
Paper Title: Quantum Computing Based Design of Multivariate Porous Materials
DOI: https://doi.org/10.1021/acscentsci.5c00918
Meanwhile, this research was supported by the Ministry of Science and ICT's Mid-Career Researcher Support Program and the Heterogeneous Material Support Program.
2025.09.09 View 1292 -
KAIST Unlocks the Secret of Next-Generation Memory
<(From Left) Professor Sang-Hee Ko Park, Ph.D candidate Sunghwan Park, Ph.D candidate Chaewon Gong, Professor Seungbum Hong>
Resistive Random Access Memory (ReRAM), which is based on oxide materials, is gaining attention as a next-generation memory and neuromorphic computing device. Its fast speeds, data retention ability, and simple structure make it a promising candidate to replace existing memory technologies. KAIST researchers have now clarified the operating principle of this memory, which is expected to provide a key clue for the development of high-performance, high-reliability next-generation memory.
KAIST (President Kwang Hyung Lee) announced on the 2nd of September that a research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with a research team led by Professor Sang-Hee Ko Park from the same department, has for the first time in the world precisely clarified the operating principle of an oxide-based memory device, which is drawing attention as a core technology for next-generation semiconductors.
Using a 'Multi-modal Scanning Probe Microscope (Multi-modal SPM)' that combines several types of microscopes*, the research team succeeded in simultaneously observing the electron flow channels inside the oxide thin film, the movement of oxygen ions, and changes in surface potential (the distribution of charge on the material's surface). Through this, they clarified the correlation between how current changes and how oxygen defects change during the process of writing and erasing information in the memory.
*Several types of microscopes: Conductive atomic force microscopy (C-AFM) for observing current flow, electrochemical strain microscopy (ESM) for observing oxygen ion movement, and Kelvin probe force microscopy (KPFM) for observing potential changes.
With this special equipment, the research team directly implemented the process of writing and erasing information in the memory by applying an electrical signal to a titanium dioxide (TiO2) thin film, confirming at the nano-level that the reason for the current changes was the variation in the distribution of oxygen defects.
In this process, they confirmed that the current flow changes depending on the amount and location of oxygen defects. For example, when there are more oxygen defects, the electron pathway widens, and the current flows well, but conversely, when they scatter, the current is blocked. Through this, they succeeded in precisely visualizing that the distribution of oxygen defects within the oxide determines the on/off state of the memory.
<Overview of the Research Process. By using one of the SPM modes, C-AFM (Conductive Atomic Force Microscopy), resistive switching corresponding to the electroforming and reset processes is induced in a 10 nm-thick TiO₂ thin film, and the resulting local current variations caused by the applied electric field are observed. Subsequently, at the same location, ESM (Electrochemical Strain Microscopy) and KPFM (Kelvin Probe Force Microscopy) signals are comprehensively analyzed to investigate and interpret the spatial correlation of ion-electronic behaviors that influence the resistive switching phenomenon>
This research was not limited to the distribution at a single point but comprehensively analyzed the changes in current flow, the movement of oxygen ions, and the surface potential distribution after applying an electrical signal over a wide area of several square micrometers (µm2). As a result, they clarified that the process of the memory's resistance changing is not solely due to oxygen defects but is also closely intertwined with the movement of electrons (electronic behavior).
In particular, the research team confirmed that when oxygen ions are injected during the 'erasing process (reset process)', the memory can stably maintain its off state (high resistance state) for a long time. This is a core principle for increasing the reliability of memory devices and is expected to provide an important clue for the future development of stable, next-generation non-volatile memory.
Professor Seungbum Hong of KAIST, who led the research, said, "This is an example that proves we can directly observe the spatial correlation of oxygen defects, ions, and electrons through a multi-modal microscope." He added, "It is expected that this analysis technique will open a new chapter in the research and development of various metal oxide-based next-generation semiconductor devices in the future."
<By combining C-AFM and ESM techniques, the correlation between local conductivity and variations in oxygen vacancy concentration after resistive switching is analyzed. After the electroforming process, regions with increased conductivity exhibit an enhancement in the ESM amplitude signal, which can be interpreted as an increase in defect ion concentration. Conversely, after the reset process, regions with reduced conductivity show a corresponding decrease in this signal. Through these observations, it is spatially demonstrated that changes in conductivity and local defect ion concentration after resistive switching exhibit a positive correlation>
This research, in which Ph.D. candidate Chaewon Gong from the KAIST Department of Materials Science and Engineering participated as the first author, was published on July 20 in 'ACS Applied Materials and Interfaces', a prestigious academic journal in the field of new materials and chemical engineering published by the American Chemical Society (ACS).
※ Paper Title: Spatially Correlated Oxygen Vacancies, Electrons and Conducting Paths in TiO2 Thin Films
This research was carried out with the support of the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.09.02 View 1508 -
KAIST achieves over 95% high-purity CO₂ capture using only smartphone charging power
Direct Air Capture (DAC) is a technology that filters out carbon dioxide present in the atmosphere at extremely low concentrations (below 400 ppm). The KAIST research team has now succeeded in capturing over 95% high-purity carbon dioxide using only low power at the level of smartphone charging voltage (3V), without hot steam or complex facilities. While high energy cost has been the biggest obstacle for conventional DAC technologies, this study is regarded as a breakthrough demonstrating real commercialization potential. Overseas patent applications have already been filed, and because it can be easily linked with renewable energy such as solar and wind power, the technology is being highlighted as a “game changer” for accelerating the transition to carbon-neutral processes.
KAIST (President Kwang Hyung Lee) announced on the 25th of August that Professor Dong-Yeun Koh’s research team from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor T. Alan Hatton’s group at MIT’s Department of Chemical Engineering, has developed the world’s first ultra-efficient e-DAC (Electrified Direct Air Capture) technology based on conductive silver nanofibers.
Conventional DAC processes required high-temperature steam (over 100℃) in the regeneration stage, where absorbed or adsorbed carbon dioxide is separated again. This process consumes about 70% of the total energy, making energy efficiency crucial, and requires complex heat-exchange systems, which makes cost reduction difficult. The joint research team, led by KAIST, solved this problem with “fibers that heat themselves electrically,” adopting Joule heating, a method that generates heat by directly passing electricity through fibers, similar to an electric blanket. By heating only where needed without an external heat source, energy loss was drastically reduced.
This technology can rapidly heat fibers to 110℃ within 80 seconds with only 3V—the energy level of smartphone charging. This shortens adsorption–desorption cycles dramatically even in low-power environments, while reducing unnecessary heat loss by about 20% compared to existing technologies.
The core of this research was not just making conductive fibers, but realizing a “breathable conductive coating” that achieves both “electrical conductivity” and “gas diffusion.”
The team uniformly coated porous fiber surfaces with a composite of silver nanowires and nanoparticles, forming a layer about 3 micrometers (µm) thick—much thinner than a human hair. This “3D continuous porous structure” allowed excellent electrical conductivity while securing pathways for CO₂ molecules to move smoothly into the fibers, enabling uniform, rapid heating and efficient CO₂ capture simultaneously.
Furthermore, when multiple fibers were modularized and connected in parallel, the total resistance dropped below 1 ohm (Ω), proving scalability to large-scale systems. The team succeeded in recovering over 95% high-purity CO₂ under real atmospheric conditions.
This achievement was the result of five years of in-depth research since 2020. Remarkably, in late 2022, long before the paper’s publication, the core technology had already been filed for PCT and domestic/international patents (WO2023068651A1, countries entered: US, EP, JP, AU, CN), securing foundational intellectual property rights. This indicates that the technology is not only highly advanced but also developed with practical commercialization in mind beyond the laboratory level.
The biggest innovation of this technology is that it runs solely on electricity, making it very easy to integrate with renewable energy sources such as solar and wind. It perfectly matches the needs of global companies that have declared RE100 and seek carbon-neutral process transitions.
Professor Dong-Yeun Koh of KAIST said, “Direct Air Capture (DAC) is not just a technology for reducing carbon dioxide emissions, but a key means of achieving ‘negative emissions’ by purifying the air itself. The conductive fiber-based DAC technology we developed can be applied not only to industrial sites but also to urban systems, significantly contributing to Korea’s leap as a leading nation in future DAC technologies.”
This study was led by Young Hun Lee (PhD, 2023 graduate of KAIST; currently at MIT Department of Chemical Engineering) and co-first-authored by Jung Hun Lee and Hwajoo Joo (MIT, Department of Chemical Engineering). The results were published online on August 1, 2025, in Advanced Materials, one of the world’s leading journals in materials science, and in recognition of its excellence, the work was also selected for the Front Inside Cover.
※ Paper title: “Design of Electrified Fiber Sorbents for Direct Air Capture with Electrically-Driven Temperature Vacuum Swing Adsorption”
※ DOI: https://doi.org/10.1002/adma.202504542
This study was supported by the Aramco–KAIST CO₂ Research Center and the National Research Foundation of Korea with funding from the Ministry of Science and ICT (No. RS-2023-00259416, DACU Source Technology Development Project).
2025.08.25 View 2650 -
KAIST to Host the ‘6th Emerging Materials Symposium’
KAIST (President Kwang Hyung Lee) announced on the 22nd of August that it will host the 6th KAIST Emerging Materials Symposium on the 26th in the Meta Convergence Hall (W13) on its main Daejeon campus, to explore the latest research trends in next-generation promising nanomaterials and discuss future visions.
Launched in 2020, this symposium marks its sixth year and has established itself as KAIST’s flagship academic event by inviting world-renowned scholars on next-generation materials to share groundbreaking achievements.
The event will feature six speakers from four prestigious overseas universities—the Massachusetts Institute of Technology (MIT), Yale University, UCLA, and Drexel University—providing an overview of cutting-edge global research trends in emerging materials, while also showcasing KAIST’s representative achievements.
Notably, Professor Yury Gogotsi of Drexel University, who gained global recognition for the pioneering development of MXene—an emerging material attracting attention for its high electrical conductivity and electromagnetic shielding capability—will deliver a lecture titled “The Future of MXene.”
In the session “Global Frontier in MIT,” three MIT professors will present the institute’s leading research: ▴Professor Ju Li, an authority on AI-robotics-based materials synthesis, ▴Professor Martin Z. Bazant, an expert in the fields of electrochemistry and electronic transport dynamics, and ▴Professor Jeehwan Kim, a leading researcher tackling the limitations of silicon wafer-based semiconductor manufacturing.
In the session “Emerging Materials and New Possibilities,” ▴Professor Yury Gogotsi of Drexel University, ▴Professor Liangbing Hu of Yale University, a pioneer in nanoparticle synthesis through rapid high-temperature thermal processing, and ▴Professor Jun Chen of UCLA, a key researcher in bioelectronic materials using multifunctional flexible materials, will present the development of core emerging materials and future directions.
Additionally, six professors from KAIST’s Department of Materials Science and Engineering will lead the session “KAIST’s MSE Entrepreneurial Spirit” where they will share the process of founding startups based on KAIST’s advanced materials technologies and how nanomaterials have taken root as foundational industries.
The session will include: ▴Professor Il-Doo Kim, founder of the nanofiber and colorimetric gas sensor company IDKLAB; ▴Professor Kibeom Kang, CEO of TDS Innovation, a company specializing in precursors and equipment for 2D material synthesis; ▴Professor Yeonsik Jeong, co-founder of Pico Foundry, a company producing SERS chips; ▴Professor Sang Wook Kim, founder of Materials Creation, which develops products based on high-quality graphene oxide; ▴Professor Jaebeom Jang, founder of Flashomic Inc., a leader in the commercialization of high-speed multiplexed protein imaging technology; and ▴Professor Steve Park, co-CEO of Aldaver, a company developing artificial cadavers (practice organs) that fully replicate the human body. They will each share their entrepreneurial cases, offering vivid lectures on the journey of scientific technologies into the marketplace.
The symposium will also feature a tour of the automated research lab at the Top-Tier KAIST-MIT Future Energy Initiative Research Center, jointly established by KAIST and MIT. The center, designed to build an AI-robotics-based autonomous research laboratory for the rapid development and application of advanced energy materials to help solve the global climate crisis, will operate for ten years. Overseas scholars will also be given an inside look at research and development using automated infrastructure, with discussions to follow on upcoming international collaborations.
Professor Il-Doo Kim of KAIST’s Department of Materials Science and Engineering, who organized the event, emphasized, “This symposium, featuring six global scholars and six KAIST entrepreneurial professors, will be a valuable opportunity to instill an international perspective and entrepreneurial mindset in students. It will also mark a turning point in KAIST’s innovative materials research and international collaborative research network.”
As part of the program, on Wednesday the 27th, KAIST will hold academic exchange sessions with overseas scholars. These will include discussions on international joint research, as well as sessions where KAIST students and early-career researchers can present their work and interact, opening opportunities for future collaborations.
The 6th KAIST Emerging Materials Symposium is open free of charge to all researchers interested in the latest research trends in chemistry, physics, biology, and materials science-related engineering fields.
Participation on the 26th will be available through on-site registration without prior application. Further details are available on the KAIST Department of Materials Science and Engineering EMS website (https://mse.kaist.ac.kr/index.php?mid=MSE_EMS).
2025.08.22 View 2146 -
Material Innovation Realized with Robotic Arms and AI, Without Human Researchers
<(From Left) M.S candidate Dongwoo Kim from KAIST, Ph.D candidate Hyun-Gi Lee from KAIST, Intern Yeham Kang from KAIST, M.S candidate Seongjae Bae from KAIST, Professor Dong-Hwa Seo from KAIST, (From top right, from left) Senior Researcher Inchul Park from POSCO Holdings, Senior Researcher Jung Woo Park, senior researcher from POSCO Holdings>
A joint research team from industry and academia in Korea has successfully developed an autonomous lab that uses AI and automation to create new cathode materials for secondary batteries. This system operates without human intervention, drastically reducing researcher labor and cutting the material discovery period by 93%.
* Autonomous Lab: A platform that autonomously designs, conducts, and analyzes experiments to find the optimal material.
KAIST (President Kwang Hyung Lee) announced on the 3rd of August that the research team led by Professor Dong-Hwa Seo of the Department of Materials Science and Engineering, in collaboration with the team of LIB Materials Research Center in Energy Materials R&D Laboratories at POSCO Holdings' POSCO N.EX.T Hub (Director Ki Soo Kim), built the lab to explore cathode materials using AI and automation technology.
Developing secondary battery cathode materials is a labor-intensive and time-consuming process for skilled researchers. It involves extensive exploration of various compositions and experimental variables through weighing, transporting, mixing, sintering*, and analyzing samples.
* Sintering: A process in which powder particles are heated to form a single solid mass through thermal activation.
The research team's autonomous lab combines an automated system with an AI model. The system handles all experimental steps—weighing, mixing, pelletizing, sintering, and analysis—without human interference. The AI model then interprets the data, learns from it, and selects the best candidates for the next experiment.
<Figure 1. Outline of the Anode Material Autonomous Exploration Laboratory>
To increase efficiency, the team designed the automation system with separate modules for each process, which are managed by a central robotic arm. This modular approach reduces the system's reliance on the robotic arm.
The team also significantly improved the synthesis speed by using a new high-speed sintering method, which is 50 times faster than the conventional low-speed method. This allows the autonomous lab to acquire 12 times more material data compared to traditional, researcher-led experiments.
<Figure 2. Synthesis of Cathode Material Using a High-Speed Sintering Device>
The vast amount of data collected is automatically interpreted by the AI model to extract information such as synthesized phases and impurity ratios. This data is systematically stored to create a high-quality database, which then serves as training data for an optimization AI model. This creates a closed-loop experimental system that recommends the next cathode composition and synthesis conditions for the automated system.
* Closed-loop experimental system: A system that independently performs all experimental processes without researcher intervention.
Operating this intelligent automation system 24 hours a day can secure more than 12 times the experimental data and shorten material discovery time by 93%. For a project requiring 500 experiments, the system can complete the work in about 6 days, whereas a traditional researcher-led approach would take 84 days.
During development, POSCO Holdings team managed the overall project planning, reviewed the platform design, and co-developed the partial module design and AI-based experimental model. The KAIST team, led by Professor Dong-hwa Seo, was responsible for the actual system implementation and operation, including platform design, module fabrication, algorithm creation, and system verification and improvement.
Professor Dong-Hwa Seo of KAIST stated that this system is a solution to the decrease in research personnel due to the low birth rate in Korea. He expects it will enhance global competitiveness by accelerating secondary battery material development through the acquisition of high-quality data.
<Figure 3. Exterior View (Side) of the Cathode Material Autonomous Exploration Laboratory>
POSCO N.EX.T Hub plans to apply an upgraded version of this autonomous lab to its own research facilities after 2026 to dramatically speed up next-generation secondary battery material development. They are planning further developments to enhance the system's stability and scalability, and hope this industry-academia collaboration will serve as a model for using innovative technology in real-world R&D.
<Figure 4. Exterior View (Front) of the Cathode Material Autonomous Exploration Laboratory>
The research was spearheaded by Ph.D. student Hyun-Gi Lee, along with master's students Seongjae Bae and Dongwoo Kim from Professor Dong-Hwa Seo’s lab at KAIST. Senior researchers Jung Woo Park and Inchul Park from LIB Materials Research Center of POSCO N.EX.T Hub's Energy Materials R&D Laboratories (Director Jeongjin Hong) also participated.
2025.08.06 View 3710 -
KAIST Ushers in Era of Predicting ‘Optimal Alloys’ Using AI, Without High-Temperature Experiments
<Picture1.(From Left) Prof. Seungbum Hong, Ph.D candidate Youngwoo Choi>
Steel alloys used in automobiles and machinery parts are typically manufactured through a melting process at high temperatures. The phenomenon where the components remain unchanged during melting is called “congruent melting.” KAIST researchers have now addressed this process—traditionally only possible through high-temperature experiments—using artificial intelligence (AI). This study draws attention as it proposes a new direction for future alloy development by predicting in advance how well alloy components will mix during melting, a long-standing challenge in the field.
KAIST (President Kwang Hyung Lee) announced on the 14th of July that Professor Seungbum Hong’s research team from the Department of Materials Science and Engineering, in international collaboration with Professor Chris Wolverton’s group at Northwestern University, has developed a high-accuracy machine learning model that predicts whether alloy components will remain stable during melting. This was achieved using formation energy data derived from Density Functional Theory (DFT)* calculations.
*Density Functional Theory (DFT): A computational quantum mechanical method used to investigate the electronic structure of many-body systems, especially atoms, molecules, and solids, based on electron density.
The research team combined formation energy values obtained via DFT with experimental melting reaction data to train a machine learning model on 4,536 binary compounds.
Among the various machine learning algorithms tested, the XGBoost-based classification model demonstrated the highest accuracy in predicting whether alloys would mix well, achieving a prediction accuracy of approximately 82.5%. The team also applied the Shapley value method* to analyze the key features of the model. One major finding was that sharp changes in the slope of the formation energy curve (referred to as “convex hull sharpness”) were the most significant factor. A steep slope indicates a composition with energetically favorable (i.e., stable) formation.
*Shapley value: An explainability method in AI used to determine how much each feature contributed to a prediction.
The most notable significance of this study is that it predicts alloy melting behavior without performing high-temperature experiments. This is especially useful for materials such as high-entropy alloys or ultra-heat-resistant alloys, which are difficult to handle experimentally. The approach could also be extended to the design of complex multi-component alloy systems in the future.
Furthermore, the physical indicators identified by the AI model showed high consistency with actual experimental results on how well alloys mix and remain stable. This suggests that the model could be broadly applied to the development of various metal materials and the prediction of structural stability.
Professor Seungbum Hong of KAIST stated, “This research demonstrates how data-driven predictive materials development is possible by integrating computational methods, experimental data, and machine learning—departing from the traditional experience-based alloy design.” He added, “In the future, by incorporating state-of-the-art AI techniques such as generative models and reinforcement learning, we could enter an era where completely new alloys are designed automatically.”
<Model performance and feature importance analysis for predicting melting congruency. (a) SHAP summary plot showing the impact of individual features on model predictions. (b) Confusion matrix illustrating the model’s classification performance. (c) Receiver operating characteristic (ROC) curve with an AUC (area under the curve) score of 0.87, indicating a strong classification performance.>
Ph.D. candidate Youngwoo Choi, from the Department of Materials Science and Engineering at KAIST, participated as the first author. The study was published in the May issue of APL Machine Learning, a prestigious journal in the field of machine learning published by the American Institute of Physics, and was selected as a “Featured Article.”
※ Paper title: Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy ※ DOI: 10.1063/5.0247514
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.14 View 2395 -
KAIST Uses AI to Discover Optimal New Material for Removing Radioactive Iodine Contamination
<(From the Right) Professor Ho Jin Ryu, Department of Nuclear and Quantum Engineering, Dr. Sujeong Lee, a graduate of the KAIST Department of Materials Science and Engineering, and Dr. Juhwan Noh of KRICT’s Digital Chemistry Research Center>
Managing radioactive waste is one of the core challenges in the use of nuclear energy. In particular, radioactive iodine poses serious environmental and health risks due to its long half-life (15.7 million years in the case of I-129), high mobility, and toxicity to living organisms. A Korean research team has successfully used artificial intelligence to discover a new material that can remove iodine for nuclear environmental remediation. The team plans to push forward with commercialization through various industry-academia collaborations, from iodine-adsorbing powders to contaminated water treatment filters.
KAIST (President Kwang Hyung Lee) announced on the 2of July that Professor Ho Jin Ryu's research team from the Department of Nuclear and Quantum Engineering, in collaboration with Dr. Juhwan Noh of the Digital Chemistry Research Center at the Korea Research Institute of Chemical Technology (KRICT, President Young Kook Lee), which operates under the National Research Council of Science & Technology (NST, Chairman Youngsik Kim), developed a technique using AI to discover new materials that effectively remove radioactive iodine contaminants.
Recent studies show that radioactive iodine primarily exists in aqueous environments in the form of iodate (IO₃⁻). However, existing silver-based adsorbents have weak chemical adsorption strength for iodate, making them inefficient. Therefore, it is imperative to develop new adsorbent materials that can effectively remove iodate.
Professor Ho Jin Ryu’s team used a machine learning-based experimental strategy to identify optimal iodate adsorbents among compounds called Layered Double Hydroxides (LDHs), which contain various metal elements.
The multi-metal LDH developed in this study – Cu₃(CrFeAl), based on copper, chromium, iron, and aluminum—showed exceptional adsorption performance, removing over 90% of iodate. This achievement was made possible by efficiently exploring a vast compositional space using AI-driven active learning, which would be difficult to search through conventional trial-and-error experiments.
<Picture2. Concept of Developed AI-Based Technology for Exploring New Materials for Radioactive Contamination Removal>
The research team focused on the fact that LDHs, like high-entropy materials, can incorporate a wide range of metal compositions and possess structures favorable for anion adsorption. However, due to the overwhelming number of possible metal combinations in multi-metal LDHs, identifying the optimal composition through traditional experimental methods has been nearly impossible.
To overcome this, the team employed AI (machine learning). Starting with experimental data from 24 binary and 96 ternary LDH compositions, they expanded their search to include quaternary and quinary candidates. As a result, they were able to discover the optimal material for iodate removal by testing only 16% of the total candidate materials.
Professor Ho Jin Ryu stated, “This study shows the potential of using artificial intelligence to efficiently identify radioactive decontamination materials from a vast pool of new material candidates, which is expected to accelerate research for developing new materials for nuclear environmental cleanup.”
The research team has filed a domestic patent application for the developed powder technology and is currently proceeding with an international patent application. They plan to enhance the material’s performance under various conditions and pursue commercialization through industry-academia cooperation in the development of filters for treating contaminated water.
Dr. Sujeong Lee, a graduate of the KAIST Department of Materials Science and Engineering, and Dr. Juhwan Noh of KRICT’s Digital Chemistry Research Center, participated as the co-first authors of the study. The results were published online on May 26 in the internationally renowned environmental publication Journal of Hazardous Materials.
※ Paper title: Discovery of multi-metal-layered double hydroxides for decontamination of iodate by machine learning-assisted experiments ※ DOI: https://doi.org/10.1016/j.jhazmat.2025.138735
This research was supported by the Nuclear Energy Research Infrastructure Program and the Nano-Materials Technology Development Program funded by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.03 View 3875