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KAIST Professor Sunkyu Han Becomes the First Korean Member of the Board of Editors of Organic Syntheses
For more than a century, Organic Syntheses has served as a unique platform beyond conventional peer reviewed journal: before publishing a procedure, its editors personally repeat the experiment in their laboratory to confirm that another chemist's submitted method actually works as reported. Professor Sunkyu Han from KAIST’s Department of Chemistry has now become the first Korean chemist elected as a member of the Board of Editors of Organic Syntheses.
KAIST (President Choongsik Bae) announced on September 11 that Professor Han has been elected as a member of the Board of Editors of Organic Syntheses at the Organic Syntheses corporate meeting, held in Chicago on August 23.
During his five-year term, Professor Han will participate to identify, solicit, and select work for publication in Organic Syntheses. He will also check synthetic procedures submitted by other researchers in his own laboratory to experimentally confirm their reliability and reproducilibity. His term may be extended for up to three additional years.
Founded in 1921, Organic Syntheses follows a publishing model unlike that of most academic journals. A synthetic procedure is published only after the member of the Board of Editors has successfully reproduced it in their laboratory and confirmed the submitted results. The journal is currently led by Editor-in-Chief Professor Rick Danheiser from the Massachusetts Institute of Technology.
Because each procedure undergoes rigorous experimental scrutiny, including the checking of its yield, selectivity, and purification process, the methods published in Organic Syntheses are widely regarded as trusted reference protocols by organic chemists around the world.
The role of a member of the Board of Editors of Organic Syntheses therefore extends well beyond conventional editors. Editors must check other researchers’ procedures and attest to their reliability, a responsibility requiring exceptional scientific judgment and experimental rigor. Over the journal’s history, many of the world’s most distinguished organic chemists have served in this capacity.
The Board of Editors currently comprises 14 members, including the Editor-in-Chief and Associate Editors, and has traditionally included two chemists based in Asia. Distinguished scholars from Japan, China, and Hong Kong have served on the board, among them Ryoji Noyori, winner of the 2001 Nobel Prize in Chemistry. Professor Han, however, is the first Korean chemist elected as a member of the Board of Editors. He will succeed Professor Pauline Chiu of the University of Hong Kong, whose term concludes later this year.
“Professor Sunkyu Han’s election as the first Korean member of the Board of Editors is a significant achievement that reflects both KAIST’s strength in basic science and the growing international stature of Korean organic chemistry,” said KAIST President Choongsik Bae. “KAIST will continue to support its researchers not only for making outstanding discoveries, but also in strengthening the reliability and advancing the frontiers of knowledge across the global academic community.”
Since its founding, Organic Syntheses has published approximately 3,100 articles. Among them, nine have been contributed by organic chemists based in Korea, four of them by KAIST faculty members.
Professor Han’s appointment will also enable KAIST’s Department of Chemistry to host the Organic Syntheses Distinguished Lecture Series during his tenure. Sponsored by Organic Syntheses, Inc., the program will bring internationally renowned organic chemists to KAIST, creating more opportunities for students and early-career researchers to engage directly with leading scholars in the field.
Professor Han is internationally recognized for his work on the chemical synthesis of complex natural products. Since joining KAIST in 2014, he has completed the synthesis of numerous structurally complex alkaloids isolated from Securinega suffruticosa, a medicinal plant native to Korea.
Recently, he has built on this expertise to expand his research into molecular photoswitches, which use light to control molecular structure and function. He is also developing anticancer agents and potential treatments for neurodegenerative disorders, including Alzheimer’s disease, using synthetic derivatives of natural products.
Since June 2025, Professor Han has also served as an Associate Editor of Organic Letters, another leading international journal in the field of organic chemistry.
“Ever since I was a student, I have relied on reactions and procedures published in Organic Syntheses and felt a profound sense of gratitude toward the chemists who personally checked those procedures and turned them into methods the broader community could trust. I have been given this opportunity thanks to everything I have learned while working alongside outstanding group members and colleagues at KAIST. I am deeply grateful to everyone who has supported me throughout that journey, as well as to the university,” said Professor Han.
He added, “I hope to follow in the footsteps of the eminent chemists who helped shape the foundation of modern organic chemistry, including Roger Adams, George Büchi, Arthur Cope, Albert Eschenmoser, Ronald Breslow, and E. J. Corey. By faithfully and diligently carrying out my responsibilities as a member of the Board of Editors of Organic Syntheses, I hope to build a body of reliable chemical knowledge that serves chemists worldwide and contributes to the continued advancement of organic chemistry.”
2026.09.11 View 131 -
KAIST Develops AI That Can Sense “That’s Not What I Meant” Without Being Told
A future in which AI can recognize a person’s unspoken “that’s not what I meant” response from brain signals and adjust its behavior on its own is coming closer. KAIST researchers have developed a technology that detects cognitive mismatch between humans and AI through brainwaves, enabling AI systems to revise their actions in real time according to human goals. The achievement is expected to accelerate the shift from AI that follows explicit commands to AI that can infer human intent.
KAIST (President Choongsik Bae) announced on September 11 that a research team led by Endowed Chair Professor Sang Wan Lee of the Department of Brain and Cognitive Sciences (Director of the Center for Neuroscience-Inspired Artificial Intelligence), in collaboration with Microsoft Research Asia (MSRA), has developed Neural Value Alignment (NVA), a next-generation brain–computer interface (BCI) technology that uses human brainwaves to align AI behavior with human intentin real time.
For AI to collaborate naturally with people, it must accurately understand what a person actually wants. Until now, however, AI systems have largely inferred human intent from externally observable information such as speech, actions, or gestures.
The challenge is that the same action can reflect different goals, and the same goal can also be achieved through different actions. For example, when a person picks up a cup, it may be unclear whether they intend to drink from it or hand it to someone else. Conversely, if the person's goal is simply to quench their thirst, that same goal could be pursued through several different actions — reaching for the cup, picking up a water bottle, or asking someone else to bring a drink. Because of this two-sided “goal–action ambiguity,” when AI misunderstands human intent, users often have to correct it through additional commands or actions.
To address this problem, the research team focused on the “prediction error” signals that arise unconsciously in the brain when a person encounters an unexpected situation. In simple terms, the team used the brain’s instant “that’s not what I meant” response when AI performs the wrong action or pursues the wrong goal.
The researchers distinguished between two types of brain responses. The first is reward prediction error (RPE), which appears when AI misunderstands the person’s ultimate goal. The second is state prediction error (SPE), which appears when the goal is correct but the process or method of action differs from what the person expected.
The team measured real-time electroencephalography (EEG) signals from people as they observed AI performing tasks. They found that the brain produced different signals depending on whether the AI misunderstood the goal itself or chose the wrong method while pursuing the correct goal. The researchers also identified distinctive brainwave patterns that appeared when both types of errors occurred simultaneously.
By applying deep learning to these brain signals, the team developed a technology that can determine, from EEG alone, how a person is interpreting the AI’s behavior. In other words, even without a person saying “that’s wrong,” the AI can recognize whether the human brain is signaling that “the goal is wrong” or “the method is wrong.”
The team then proposed a Neural Value Alignment-based human–AI synergy algorithm, which feeds these decoded brain signals back to the AI in real time so that it can correct its own behavior.
When the AI detects an SPE signal, it interprets the situation as “the desired goal is correct, but the method is wrong” and adjusts its action strategy. When it detects an RPE signal, it understands that “the goal itself was misunderstood” and searches again for what the person truly intended.
Simulation results showed that the proposed method adapted more quickly than existing approaches even in uncertain situations, such as when a person’s goal suddenly changed or some human neural feedbacks were missing.
The key significance of this research is that it demonstrates the possibility of AI systems correcting themselves by reading a person’s unconscious “that’s not what I meant” brain response, without requiring the user to repeatedly say “do it this way” or “that’s not right.”
As the technology advances, it could be applied to physical AI robots in homes and industrial settings, allowing them to understand user intent more naturally and adjust their actions accordingly. It could also be extended to autonomous vehicles that quickly reflect driver judgment, medical and rehabilitation robots for patients who have difficulty speaking or moving, and educational AI systems that adapt to a student’s cognitive state.
Ultimately, the study presents a new model of human–AI collaboration, moving beyond AI that acts only when explicitly instructed toward AI that can sense human responses and adjust itself accordingly.
Professor Sang Wan Lee, who led the international collaboration, said, “This research is meaningful because it shows that AI can move beyond inferring human intent only from visible behavioral outcomes and instead directly use cognitive signals generated in the brain during collaboration with AI.” He added, “The technology can be expanded to a wide range of fields where human judgment and AI behavior must be closely connected, including physical AI, BCI, autonomous driving, precision personalized education, medical robotics, and human–computer interaction.”
Miran Lee, Director, Microsoft Research Accelerator at Microsoft Research, said, “This achievement is the result of the ongoing international collaboration between KAIST and Microsoft Research Asia. We look forward to continuing this partnership to develop world-class BCI technologies that enable humans and AI to communicate and collaborate more naturally.”
The study’s first author is Xin Xu, a Ph.D. student in KAIST’s Department of Brain and Cognitive Sciences. Researchers from Microsoft Research Asia, including Yansen Wang, Dongqi Han, and Dongsheng Li, also participated in the study. The research was published online in August 2026 in IEEE Transactions on Cybernetics, an international journal in the field of cybernetics.
Paper title: Neural Value Alignment: Human–AI Collaboration Under Goal–Action Ambiguity
DOI: 10.1109/TCYB.2026.3722605
Another KAIST–Microsoft Research Asia collaborative study on helping AI rapidly adapt to continuously changing environments was presented in June at ICML 2026, one of the leading international conferences in artificial intelligence. The study was led by Niklas Koeppe, a Ph.D. student in KAIST’s Program of Brain and Cognitive Engineering, as first author.
Paper title: Mitigating Plasticity Loss through Architectural Design in Continual Learning
Original paper: https://icml.cc/virtual/2026/poster/61534
This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP), funded by the Ministry of Science and ICT.
2026.09.10 View 263 -
KAIST Signs MOU with Université Paris-Saclay on Behalf of K-STAR
KAIST (President Choongsik Bae) announced on September 9 that, representing K-STAR, a consortium comprising KAIST, GIST, DGIST, UNIST, and POSTECH, it signed a memorandum of understanding (MOU) with Université Paris-Saclay on September 7 to strengthen research cooperation. The signing ceremony took place at Bâtiment Bréguet on the university’s campus.
The agreement was concluded on the occasion of President Lee Jae-myung’s state visit to France. It follows up on plans discussed at the ninth Korea-France Joint Committee on Science and Technology, held in Seoul in April, to expand scientific, technological, and academic exchanges between K-STAR and French universities.
Université Paris-Saclay is one of France’s leading research universities. It is home to a large research and education ecosystem encompassing universities, grandes écoles, and major national research organizations, including the French National Centre for Scientific Research (CNRS) and the French Alternative Energies and Atomic Energy Commission (CEA). The university has internationally recognized strengths across a broad range of fields, from mathematics and physics to artificial intelligence, quantum technology, biology, and engineering.
Under the agreement, K-STAR and Université Paris-Saclay will establish a practical framework for cooperation encompassing student exchanges, mobility programs for early-career researchers, and joint research. They plan to begin by offering research internships through which undergraduate and graduate students can gain experience in laboratories at partner institutions. They will also expand opportunities for exchanges and collaboration among postdoctoral researchers and early-career faculty members.
The two sides will promote joint research in key scientific and technological fields, including artificial intelligence, quantum technology, advanced biotechnology, and advanced materials. They will also facilitate shared access to research infrastructure and organize regular workshops and symposia, enabling researchers to maintain sustained exchanges and develop new collaborative research projects.
The agreement strengthens “Global Connect,” KAIST’s strategy for international cooperation. Through this initiative, KAIST seeks to move beyond conventional university exchanges by closely integrating research, education, and talent mobility. It also aims to expand its global research network in partnership with Korea’s other science and technology-focused universities.
“Global cooperation must move beyond conventional exchanges and evolve into partnerships that combine complementary strengths to create new knowledge and cultivate talent together. By connecting the research capabilities of Université Paris-Saclay and K-STAR, we expect to generate new research achievements in critical fields such as AI, quantum technology, and biotechnology, while further strengthening Korea’s global network in science and technology,” said KAIST President Choongsik Bae.
The agreement is also significant because it expands the educational and research cooperation that KAIST has developed with France over the past 15 years to the K-STAR level.
KAIST also signed a student exchange agreement with CentraleSupélec, a grande école affiliated with Université Paris-Saclay, in 2011. The two institutions subsequently established a dual-degree program in mechanical engineering in 2017 and have continued to develop their partnership. Building on the experience and mutual trust accumulated through this cooperation, the new agreement broadens the partnership to encompass K-STAR and Université Paris-Saclay.
Université Paris-Saclay President Camille Galap said, “Université Paris-Saclay and K-STAR have broad research capabilities spanning basic science and advanced technology, giving this partnership considerable potential. I hope the agreement will promote more active exchanges among students and researchers from both countries and enable us to achieve new scientific advances through joint research.”
Jaemin Jung, KAIST Senior Vice President for Planning and Budget, said, “This agreement marks the beginning of our efforts to translate the university-level cooperation discussed at the ninth Korea-France Joint Committee on Science and Technology in April into concrete research collaboration. KAIST will support exchanges among students and researchers so that they can work in one another’s laboratories and produce tangible outcomes, including joint research projects, publications, and the shared use of research infrastructure.”
The signing ceremony was also attended by Stéphane Nonnenmacher, Director of the Graduate School of Mathematics at Université Paris-Saclay; Divya Madhavan, Director of International Relations at CentraleSupélec; and a delegation from the Korea Institute for Advanced Study (KIAS), a KAIST-affiliated institute.
2026.09.09 View 217 -
Vitamin A Derivative Preserves T Cells’ Function as KAIST Points to a New Strategy for Treating Intractable Brain Tumors
Immune cells can become exhausted after prolonged exposure to cancer, gradually losing their ability to attack tumor cells. This phenomenon is particularly pronounced in aggressive brain tumors and can severely limit the effectiveness of immunotherapy. A KAIST research team has now discovered that all-trans retinoic acid (ATRA), a vitamin A derivative, may help prevent such exhaustion and enhance the efficacy of immune checkpoint inhibitors.
KAIST (President Choongsik Bae) announced on September 8 that a research team led by Professor Heung Kyu Lee from the Department of Biological Sciences, in collaboration with researchers from Seoul St. Mary's Hospital, and Konyang University College of Medicine, has found that all-trans retinoic acid (ATRA), a form of active vitamin A metabolite, suppresses the "terminal exhaustion" of CD8⁺ T cells that attack cancer cells within brain tumors, and can enhance the effect of anti-PD-1 immunotherapy. Anti-PD-1 is a leading immuno-oncology drug (immune checkpoint inhibitor).
Glioblastoma is a representative form of intractable brain cancer that frequently recurs even after surgery, radiation, and chemotherapy. Immune checkpoint inhibitors have shown only limited effect against the disease. One key reason is that immune cells that infiltrate the tumor become exhausted after prolonged combat and lose their functional capacity.
Cells that reach a state of terminal exhaustion in particular lose their ability to kill cancer cells, much like soldiers who have exhausted themselves in prolonged combat. In this state, achieving sufficient therapeutic effect is difficult even with anti-PD-1 therapy, which releases the "brake" cancer cells impose on immune cells.
To address this problem, the research team tried to figure out how to prevent immune cells from reaching total burnout, rather than how to revive the cells. Subsequently, the research team focused on the signaling of ATRA, an active vitamin A metabolite known to regulate cell differentiation and function.
Using an in-vitro model that mimicked the hypoxic, exhaustion-promoting tumor microenvironment of brain tumors, the team induced exhaustion in CD8⁺ T cells. Those conditioned with ATRA progressed to terminal exhaustion at a markedly lower rate. These cells also produced higher levels of immune molecules essential for attacking cancer — including interleukin-2 (IL-2), interferon gamma (IFN-γ), and tumor necrosis factor alpha (TNF-α) — and retained their ability to kill brain tumor cells.
The team also confirmed that ATRA activates WNT/β-catenin signaling within CD8⁺ T cells and increases TCF-1βBD, a TCF-1 isoform important for maintaining T-cell function. In simple terms, the vitamin A metabolite turns on a "function-preserving switch" inside immune cells, helping them avoid complete exhaustion even during prolonged combat with cancer cells.
The effect was also confirmed in mouse glioma models. CD8⁺ T cells conditioned with ATRA maintained better immune function within the tumor, and oral administration of ATRA alone also increased both the number and function of tumor-infiltrating CD8⁺ T cells. As tumor burden decreased, survival was also extended. Notably, in a model of recurrent brain tumors, anti-PD-1 immunotherapy alone showed only limited effect, but combining it with ATRA substantially improved tumor suppression and long-term survival outcomes.
In other words, if anti-PD-1 therapy releases the "brake" imposed on immune cells, ATRA keeps their "battery" from running completely dead. By combining the two approaches, the researchers propose a new combination-therapy strategy that could help overcome the limitations of existing immunotherapy.
The team also analyzed publicly available human glioblastoma datasets. In single-cell transcriptomic data from patients treated with anti-PD-1 therapy, CD8⁺ T cells from responders showed higher retinoic-acid-responsive and WNT signaling gene signatures than those from non-responders. Separately, in an immunotherapy-naïve glioblastoma cohort, patients with a higher proportion of retinoic-acid-responsive CD8⁺ T cells showed significantly better overall survival. The team noted that this study does not directly demonstrate ATRA's therapeutic efficacy in patients, and that further clinical research will be needed to confirm appropriate administration methods, dosages, and combination effects with immunotherapy drugs before it can be applied to actual brain tumor treatment.
Professor Heung Kyu Lee said, "Glioblastoma is one of the cancers most resistant to immunotherapy, because immune cells within the tumor readily become exhausted." He added that the study is meaningful for presenting a molecular mechanism by which active vitamin A signaling helps prevent the terminal exhaustion of CD8⁺ T cells while preserving their anti-cancer function. He also noted that future work validating more precise delivery methods or combination strategies could establish this as a new approach for improving the responsiveness of immunotherapy against intractable brain tumors.
The study was conducted with Dr. In Kang, a postdoctoral researcher in KAIST's Department of Biological Sciences, as first author, and Professor Heung Kyu Lee as corresponding author. Professor Jae-Byum Chang from KAIST's Department of Materials Science and Engineering, Professor Sung Ki Lee from Konyang University College of Medicine, and Professor Stephen Ahn from Seoul St. Mary's Hospital also participated in the research. The findings were published on August 26 in the international journal Signal Transduction and Targeted Therapy.
Paper title: All-trans retinoic acid suppresses CD8⁺ T-cell terminal exhaustion and potentiates anti-PD-1 therapy in glioblastoma
DOI: 10.1038/s41392-026-02852-9
This work was supported by National Research Foundation of Korea grants (RS-2023-NR077244, RS-2024-00439735, RS-2026-25509011 to H.K.L. and RS-2024-00352668 to S.A.). This study was also supported by the Samsung Science and Technology Foundation (SSTF-BA1902-05 to H.K.L.), Republic of Korea. This research was also supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS-2023-00302794 to J.B.C.).
2026.09.08 View 543 -
KAIST Identifies Cause of Artifacts in Battery Nanoscale Analysis, Paving the Way for More Reliable Measurements
A signal that appears to show ions moving inside a battery may, in fact, be an illusion caused by an uneven surface. A KAIST research team has identified the origin of this type of artifacts, which can lead researchers to misinterpret what is happening inside a battery, and has developed a method to reduce it. The findings are expected to enable more accurate analysis of ion movement and improve the reliability of next-generation battery-material development, including that of solid-state and sodium-ion batteries.
KAIST (President Choongsik Bae) announced on September 7 that a research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the research groups of Professor Jong Min Yuk from the same department and Professor Nam-Soon Choi from the Department of Chemical and Biomolecular Engineering, has identified the cause of a measurement artifact in nanoscale battery analysis that can be mistaken for actual ion transport. The team also proposed a method for effectively reducing this artifact.
During charging and discharging, lithium or sodium ions move back and forth within a battery. The speed and ease with which these ions move affect the battery’s performance and lifespan. Developing better batteries therefore requires researchers to precisely determine where ions can move freely and where their movement is hindered.
One technique used for this type of analysis is Electrochemical Strain Microscopy (ESM), which is based on Atomic Force Microscopy (AFM). ESM scans the surface of a battery material with an extremely fine tip and measures nanoscale changes in the material associated with ion movement, allowing researchers to indirectly track ion transport.
The problem is that when the surface of a battery material is rough, similar signals can appear even in the absence of actual ion movement. If these signals are interpreted as evidence of ion transport, researchers may incorrectly identify where ions are moving within the material.
To investigate the origin of theseartifactss, the team created fine trenches on the surface of an ionically inactive single-crystal silicon sample. This provided an experimental environment in which no ions were moving, while the sample surface remained uneven.
The results quantitatively demonstrated that variations in surface height alone can alter the degree of contact between the microscope tip and the sample, producing signals similar to those generated by actual ion movement.
The same phenomenon was also observed in actual battery materials. When the team analyzed a graphite anode and the sodium solid electrolyte Na₂Zn₂TeO₆, the ESM signals likewise varied according to surface topography. This confirmed that the issue is not limited to a particular material but is a phenomenon that researchers must account for when conducting nanoscale analyses of a wide range of battery materials.
As a solution, the team proposed making the surfaces of battery materials as smooth and flat as possible. To achieve this, the researchers used a cooling cross-section polisher (CCP), which employs an argon (Ar) ion beam to precisely polish sample cross sections. Because argon is chemically inert under most conditions, this technique allows the surface to be processed precisely without significantly altering the properties of the sample.
This treatment substantially reduced surface roughness and, in turn, decreased measurement artifacts caused by uneven surfaces. The mechanism is comparable to a car moving up and down while traveling over a bumpy road: as the scanning tip passes over height variations on the surface of a battery material, the degree of contact between the tip and the sample changes. These changes can generate signals resembling those produced by actual ion movement.
In particular, the team examined signals detected at grain boundaries—the interfaces at which the small crystals that make up a battery material meet, much like the seams between adjacent tiles.
Before the surface was smoothed, strong ESM signals appeared at these grain boundaries. After the surface was polished, however, the enhanced signals disappeared. This finding indicates that some signals previously interpreted as evidence of “pathways that facilitate ion transport” may actually have resulted from variations in surface height rather than genuine ion movement.
This study is significant because it experimentally demonstrates how this type of measurement artifact arises in nanoscale battery analysis and shows that it can be reduced using the practical approach of smoothing battery-material surfaces.
The findings are expected to provide a more accurate understanding of where ions move freely and where their movement is hindered within a battery. Such insights could provide an important foundation for designing battery materials that facilitate ion transport, thereby enabling faster charging and longer battery life.
The team expects this analytical approach to be applicable not only to widely used lithium-ion batteries but also to next-generation battery systems. These include solid-state batteries, which use solid rather than liquid electrolytes, and sodium-ion batteries, which use sodium ions in place of lithium ions. The approach could help researchers more accurately understand how these batteries operate and support the design of new materials.
Furthermore, the accumulation of reliable nanoscale analysis data could provide high-quality training datasets for artificial intelligence (AI) and machine-learning research aimed at designing new battery materials and predicting their performance.
“This research clearly demonstrates how variations in surface height affect the results of nanoscale battery-material analysis,” said Professor Hong. “We expect our findings to enable more accurate tracking of ion movement within batteries and contribute to understanding the operating mechanisms of next-generation battery materials and designing improved materials.”
Dongyan Chen, a PhD student in the Department of Materials Science and Engineering, served as the first author of the study, which was published in Small Methods, an international journal specializing in materials science and nanotechnology.
Paper title: Quantitative Analysis of Topographic Crosstalk in DART-ESM Arising from Feedback-Loop-Delay-Induced Contact Stiffness Variations in Battery Materials
DOI: https://doi.org/10.1002/smtd.70763
This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government’s Ministry of Science and ICT (MSIT) (Nos. RS-2026-25468150 and RS-2023-00247245).
2026.09.07 View 482 -
Weak Hydrogen Bonds Dethrone Copper's Stability, Opening a New Path for Ion Selection
Copper has been knocked off the top of a stability ranking it had dominated for decades. Without altering the atoms directly bonded to the metal, a KAIST research team reversed the longstanding trend in which copper generally forms the most stable complexes by tuning only the weak hydrogen bonds in its surrounding environment. The findings could open new avenues for selective metal separation and recognition, as well as catalyst design.
KAIST (President Choongsik Bae) announced on September 6 that a research team led by Professor Yunjung Baek of the Department of Chemistry developed a "metal complex"—a structure in which several molecules surround and bond to a central metal— using a ligand based on the flavin framework found in vitamin B2. By tuning the hydrogen bonding around the metal, the team achieved a stability trend that runs opposite to the widely accepted Irving–Williams series.
The Irving–Williams series is an empirical rule that ranks how stably transition metals—such as iron, nickel, and copper, which bond with other substances in a variety of ways—bind to surrounding molecules. Among manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), and zinc (Zn), stability is generally known to increase moving from manganese toward copper, with copper forming particularly stable bonds.
This difference has been understood to arise from each metal's electronic structure, meaning how its electrons are arranged. In other words, which metal forms the more stable complex has long been considered largely determined by the metal's own inherent properties.
Until now, changing this order typically required either designing a new ligand, the molecule that directly grips the metal, or altering the coordination structure, the way the metal bonds with surrounding molecules.
The research team instead focused on hydrogen bonding, a force that acts outside the direct metal bonding region. Hydrogen bonds are relatively weak forces between molecules that help hold the surrounding structure in a fixed shape.
Using flavin derivatives, versions of flavin with part of their chemical structure modified, the team incorporated different metals ranging from manganese to zinc, while ensuring that all the metals shared the same basic coordination geometry. By keeping the basic conditions around each metal identical, the researchers were able to examine what difference hydrogen bonding alone made to each metal's stability.
The results showed that hydrogen bonding specifically blocks the structural change copper needs to become stable. Copper has a distinctive tendency to slightly reshape its surrounding bonding structure into a form that favors its own stability, much like a person shifting slightly to find the most comfortable posture.
In the structure developed by the team, however, the surrounding hydrogen-bonded framework constrained the geometry around copper, preventing it from adopting its preferred distorted structure. As a result, copper lost much of the additional stabilization it would normally gain through structural distortion, producing what the researchers describe as an anti–Irving–Williams trend.
What matters most is not simply that copper was displaced from the top of the ranking, but that the study demonstrated the relative stability of metal complexes, long regarded as being largely determined by the intrinsic properties of each metal, can be adjusted by changing the surrounding environment. For example, if a desired metal can be made to bond more strongly while others bond more weakly within a mixture, the principle could provide a basis for developing systems that selectively extract or recover target metals.
This principle could also be applied to catalyst design, where the surrounding environment is tuned so that a desired metal performs more effectively. Just as proteins and enzymes in the human body select the metal they need from among iron, copper, zinc, and others, the approach is also expected to offer a new method for designing biomimetic systems that replicate the operating principles of living organisms to achieve a desired function.
Professor Yunjung Baek said, "The key point of this study is not simply that we lowered copper's stability, but that we showed the order of bonding stability, long regarded as an inherent property of each metal, can be changed through the surrounding environment." She added that the approach is expected to be used to design new chemical systems that selectively capture or react with a desired metal.
The research also drew attention at the International Conference on Coordination Chemistry (ICCC), held in Denmark. Haneul Im, a combined master's and PhD student in KAIST's Department of Chemistry and the study's first author, presented the work as a poster and was the only Korean student to receive a Best Poster Award. The study, with Haneul Im as first author, was published in the Journal of the American Chemical Society (JACS) on September 3. JACS published by the American Chemical Society (ACS).
Paper title: When Copper Falls: Overriding the Irving–Williams Stability Trend through Outer-Sphere Hydrogen Bonding,
DOI: 10.1021/jacs.6c10430
Author information: Haneul Im (KAIST, first author), Neetu Singh (KAIST, joint second author), Seogyeon Kwon (IBS, joint second author), Changhyeon Seo (KAIST, third author), Nak-Kwan Chung (KRISS, fourth author), and Yunjung Baek (KAIST, corresponding author). Six authors in total.
This work was supported by the Young Scientist Grants program of the Ministry of Science and ICT (MSIT).
2026.09.07 View 336 -
KAIST Professor Jaekyung Kim Selected as One of Asia's 12 Next-Generation Scientists
KAIST (President Choongsik Bae) announced on September 4 that Professor Jaekyung Kim from the Department of Biological Sciences has been selected as a Fellow of the “2026 Asian Young Scientist Fellowship (AYSF),” a program that supports promising young scientists across Asia.
The Asian Young Scientist Fellowship (AYSF) is a privately funded research fellowship in Asia, established to support young scientists in carrying out creative and challenging research. Since selecting its first cohort of Fellows in 2023, the AYSF selects 12 early-career researchers each year across the fundamental science disciplines of life sciences, physical sciences, and mathematics and computer science. Each selected Fellow receives a total of $100,000 USD over two years to support their research.
The AYSF does more than support young scientists across individual academic disciplines —it actively encourages interdisciplinary research that transcends traditional disciplinary boundaries to open new research directions. It places particular emphasis on supporting young scientists at the critical stage of launching their careers as independent researchers, helping them develop creative ideas and pioneer new areas of research.
Candidates are nominated from among full-time researchers at universities or research institutions in Asia who are within 10 years of completing their terminal degree (Ph.D./M.D.). Among the nominated candidates, a Selection Committee composed of scientists from Asia and around the world conducts a comprehensive evaluation of each candidate’s research achievements and future research potential.
This year, 12 early-career researchers in Asia were selected as 2026 Fellows Professor Kim was named a Fellow in the life sciences category, alongside Professor Mikyung Shin of Sungkyunkwan University, making them the two Fellows affiliated with Korean universities. Previous AYS Fellows from Korea include Professor Kyeongsu Choi of the Korea Institute for Advanced Study (KIAS) in 2023; Professor Jiheong Kang of Seoul National University in 2024; and Professor Seongjun Park of Seoul National University and Professor In-Jee Jeong of KIAS in 2025.
Professor Kim studies how the brain organizes experiences and information into memories during sleep. Using a systems- and computational-neuroscience approach, he focuses on memory consolidation—the process by which information learned during the day becomes stabilized into long-term memory during sleep—and the neural mechanisms involved. He also analyzes biological signals related to dreaming and investigates how sleep affects higher cognitive functions such as creative thinking.
Professor Kim received his bachelor's degree from Hanyang University and his Ph.D. from KAIST, and completed postdoctoral research at the University of California, San Francisco (UCSF) and the San Francisco VA Medical Center. He joined the KAIST Department of Biological Sciences in 2023 and currently leads the Neural Processing Lab.
The 2026 AYS Fellows will attend the 2026 AYSF Annual Conference on November 9, 2026, at the University of Hong Kong to showcase their research work and innovative ideas.
2026.09.04 View 464 -
KAIST Develops AI Technology That Fixes SQL Errors Without Starting Over
“Find the best-selling product from last year.” When an AI system attempts to answer a question like this by querying a company database, even a single reference to a nonexistent item can cause the query to fail. Until now, correcting such an error often required regenerating the entire SQL query from scratch. A KAIST research team has developed a technology that instead identifies and fixes only the erroneous part. The technology is expected to make AI-powered data retrieval faster and more accurate, accelerating the adoption of AI work assistants in enterprise environments.
KAIST (President Choongsik Bae) announced on September 4 that a research team led by Professor Min-Soo Kim from the School of Computing has developed SafeQL, a technology that detects and corrects errors that arise when natural-language questions are translated into Structured Query Language (SQL).
Text-to-SQL technology enables AI systems to convert everyday questions, such as “Which product saw the largest increase in sales last year?” or “Which items are running low in stock?”, into SQL queries. This allows users to retrieve sales, customer, and inventory data using natural language without having to understand complex database commands.
However, AI systems can make mistakes when generating SQL. For example, they may refer to a table or column that does not exist or join tables incorrectly. These errors can prevent the query from running and leave the AI unable to retrieve the requested data.
Conventional correction methods send the database error back to a large language model (LLM) and ask it to regenerate the entire query. This is similar to rewriting an entire report to correct a single word. In the process, parts that were already correct may be altered, new errors may be introduced, and repeated calls to the LLM increase both cost and processing time.
SafeQL takes a different approach. Instead of discarding and regenerating the entire query after an execution failure, it interprets feedback from the database management system to precisely locate the faulty component, such as a relation, attribute, function, or value. It then incrementally repairs that component while preserving the valid structure and logic of the original query.
To achieve this, the research team developed a “safe query space” approach. Among the candidate corrections that can be executed on the database, SafeQL searches for the one closest to the query originally generated by the AI. The system prioritizes the most promising candidates and filters out unsuitable ones in advance, reducing the time required for correction.
The team implemented SafeQL as a PostgreSQL extension and integrated it with the database system’s parser, binder, and type analyzer. This enables SafeQL to precisely locate errors even in complex SQL queries. The system calls the LLM again when search-based refinement cannot resolve the error within a predefined threshold, thereby reducing unnecessary AI use.
The research team evaluated SafeQL using BIRD and Spider, two widely used benchmarks for assessing the database querying capabilities of AI systems.
On the BIRD benchmark, SafeQL resolved execution errors in up to 87.4% of initially erroneous SQL queries and improved execution accuracy by up to 5.8 percentage points over the unrefined baseline. Compared with regenerating the entire query, SafeQL reduced token use by a factor of up to 15.1 and refinement latency by a factor of up to 29.6.
SafeQL is expected to be particularly useful for enterprises that handle large volumes of data requests. If an error occurs while an AI system is searching internal sales, customer, or inventory data, SafeQL can repair only the affected part instead of regenerating the entire query. This can reduce the cost and time required to operate enterprise AI systems and support reliable work automation powered by AI agents and corporate data.
Professor Min-Soo Kim said, “For AI to perform real-world tasks in enterprise environments, it must be able to accurately retrieve the data it needs.”
He added, “When AI makes an error during a database search, SafeQL fixes only the affected part instead of starting over from scratch. By reducing errors, costs, and processing time, we expect the technology to accelerate reliable AI-powered work automation.”
Geonho Lee, a PhD student in the KAIST School of Computing, participated in the study as first author, with Professor Min-Soo Kim serving as corresponding author. The findings will be presented at the International Conference on Very Large Data Bases (VLDB), a leading international database conference, to be held in Boston, USA, from September 1 to 5.
Paper title: SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL
DOI: 10.14778/3819518.3819545
Authors: Geonho Lee (KAIST, first author) and Min-Soo Kim (KAIST, corresponding author)
This research was supported by the National Research Foundation of Korea (NRF) and the SW Star Lab program of the Institute of Information & Communications Technology Planning & Evaluation (IITP), both funded by the Ministry of Science and ICT (MSIT).
2026.09.04 View 527 -
KAIST Tames a Semiconductor Greenhouse Gas 6,000 Times More Potent Than CO₂ with the ‘Power of Disorder’
Among the gases used in semiconductor manufacturing, tetrafluoromethane (CF₄) is a greenhouse gas over 6,000 times more potent than carbon dioxide. A KAIST research team has developed a technology that removes this gas with high efficiency while extending the usable lifetime of the catalyst that helps break it down by harnessing the ‘power of disorder,’ in which mixing multiple metal atoms together actually stabilizes the catalyst’s structure.
KAIST (President Choongsik Bae) announced on September 3 that a research team led by Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering, working in collaboration with researchers from Samsung Electronics, has developed a new catalyst capable of removing CF₄, a greenhouse gas used in processes such as the fabrication of fine semiconductor circuits with high efficiency over long periods of use.
CF₄ is used in processes such as dry etching, in which unwanted portions of a semiconductor wafer are selectively removed to create fine circuit patterns. The problem lies in the CF₄ left over after use. Because its carbon and fluorine atoms are bound together extremely tightly, the gas does not easily decompose, and once released into the atmosphere, it can persist for roughly 50,000 years. Its impact on global warming is also more than 6,000 times greater than that of carbon dioxide.
To prevent CF₄ from being released as is, semiconductor manufacturing sites currently decompose it at high temperatures using steam and a catalyst. A catalyst speeds up chemical reactions, much like those used to reduce pollutants in car exhaust.
However, conventional catalysts have suffered from declining performance the longer they are used. This is because hydrogen fluoride (HF), generated as CF₄ decomposes, combines with moisture to create a highly corrosive environment, causing the catalyst’s fine particles to aggregate or its structure to change. When small catalyst particles clump together into larger masses, the surface area in contact with the CF₄ to be treated shrinks, and performance declines accordingly.
The research team solved this problem, paradoxically, by harnessing the ‘power of disorder.’
Mixing multiple atom types creates a complex, disordered structure that resists phase changes and remains stable. This process is called entropy stabilization. In simple terms, it is a principle in which evenly mixing multiple kinds of atoms makes it difficult for a catalyst to clump together or change into another structure.
Using this principle, the research team evenly incorporated multiple metals — aluminum (Al), zinc (Zn), gallium (Ga), nickel (Ni), and cobalt (Co) — into a single aluminate crystal structure. Aluminate is a material in which several metals are bonded around a basic framework of aluminum and oxygen. Through this approach, the team developed an ‘entropy-stabilized aluminate (ESA) catalyst’ that resists aggregation and structural deformation even under the harsh conditions of high temperature, moisture, and fluorine occurring together.
The performance gap was clear. The new catalyst’s intrinsic activity for decomposing CF₄ was approximately 2.3 times higher than that of a conventional alumina catalyst. Notably, in an accelerated test conducted at about 800°C for 150 hours, the CF₄ conversion of the conventional alumina catalyst dropped from 93% to 48%. The new catalyst, by contrast, maintained a high level, declining only from 98% to 92%. This demonstrated that the catalyst can remove CF₄ with high efficiency while sustaining its performance over extended periods.
The researchers also revealed the decomposition mechanism of CF₄. To do this, they used oxygen isotopes, which allow the movement of oxygen atoms to be tracked. In simple terms, this involves attaching a ‘tag’ to oxygen atoms so that where the oxygen comes from and where it moves to during the reaction can be traced.
The results confirmed that the catalyst first uses the oxygen within its own structure to decompose CF₄, and that the reaction continues as surrounding steam replenishes the oxygen that has been depleted. In effect, the catalyst functions as a kind of ‘oxygen refill system,’ in which steam restores the oxygen the catalyst draws upon. Through this, the research team provided the world’s first experimental confirmation of a CF₄ decomposition process that had previously only been proposed in theory.
The significance of this research goes beyond developing a single catalyst that decomposes CF₄ effectively; it presents a new catalyst design strategy capable of achieving both high decomposition performance and a long service life at the same time. The approach is expected to be applicable to the future development of catalysts for treating a range of semiconductor process gases by varying the types and combinations of metals used.
Professor Choi said, “By applying the principle that disorder in nature can actually make a structure more stable to catalyst design, we achieved both high CF₄ decomposition performance and long-term stability at the same time.” He added, “This work is meaningful in that it presents a new materials design strategy that can be extended to catalysts for treating a range of semiconductor process gases by varying the types and combinations of metals used.”
The study was led by Dr. Seunghyuck Chi, a postdoctoral researcher in KAIST’s Department of Chemical and Biomolecular Engineering, who served as first author, with researchers from Samsung Electronics participating as co-authors. The findings were published in June in the international chemistry journal Angewandte Chemie International Edition.
Paper title: Entropy-Stabilized Aluminate Catalysts that Break the Activity–Stability Tradeoff in CF₄ Hydrolysis,
DOI: 10.1002/anie.6752036
This research was supported by the National Research Foundation of Korea (RS‐2024‐00333937 and RS‐2024‐00405261).
2026.09.03 View 538 -
KAIST Skin-Conformable Micro-LED Mask Boosts Skin Rejuvenation, Brightening, and Synergistic Benefits with Polynucleotide (PN) Injections
Home beauty devices that let users care for their skin conveniently at home have grown popular recently, but conventional LED masks are limited not only by their rigid structures but also by their point-emitting LEDs, which must be positioned away from the skin to spread light over a broader area. This inherently prevents close skin contact and increases optical loss.
KAIST (President Chung-Sik Bae) announced on September 2 that a joint research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering confirmed skin-brightening and elasticity-improving effects using a face-conforming LED mask. The mask combines a flexible surface-emitting micro-LED layer, consisting of a dense micro-LED array and a light-diffusing layer for uniform illumination, with a three-dimensional elastic scaffold that conforms to facial contours.
In a 2024 study published in Advanced Materials, Professor Lee clinically demonstrated that a flexible surface-emitting micro-LED mask produced up to 340% greater improvement in deep skin elasticity than conventional LED masks.
In the present study, the 3D elastic scaffold adapted to different facial contours, increasing skin-contact area from 46.9% to 78.1% and reducing the light-source-to-skin distance to 1.8 mm, while achieving 93.83% light uniformity across eight facial measurement sites.
The researchers evaluated the mask in a split-face clinical study involving 33 participants. All of the participants received PN injections across the entire face, while the micro-LED mask was applied to only one side for eight weeks. The micro-LED-treated side showed greater improvement across all five skin-brightening indices, including skin brightness, tone uniformity, skin exfoliation, melasma count, and melasma area. Consistent with the clinical findings, human-derived skin tissue treated with micro-LEDs also showed reduced expression of the melanogenesis-related markers MITF and TYR, supporting a direct contribution of the LED treatment to the brightening effect. PN injections are primarily known for skin rejuvenation, with limited evidence of a direct skin-brightening effect when used alone.
The synergy between PN injection and the micro-LED mask was also evident in skin regeneration and post-procedure recovery. Deep skin elasticity improved by 12.8% on the LED+PN side, compared with 3.1% on the PN-only side, representing approximately 4.1 times the improvement observed with PN alone. Skin-barrier recovery was faster, while post-procedure redness was reduced to a greater extent, consistent with the known anti-inflammatory and tissue-repair effects of red-light photobiomodulation. These findings suggest that the LED mask may boost mitochondrial ATP production in the skin and activate regenerative responses that PN alone cannot fully induce.
Professor Lee said, “This study clinically demonstrates that home-use LED masks can extend beyond skin rejuvenation to skin brightening and highlights the importance of delivering light in close contact with the skin. When combined with in-clinic skin-rejuvenation injections, the mask can substantially improve skin elasticity and accelerate post-procedure recovery, creating a new clinic-to-home care platform.”
This study was conducted jointly by researchers from KAIST and AMOREPACIFIC. A related product based on the technology is scheduled to launch in Japan in Q4 2026 and enter the U.S. market in Q1 2027.
The resulting paper, titled “Clinical validation of skin brightening and rejuvenation enabled by a skin-conformable surface-emitting micro-LED mask with injection,” was published in Nano Energy
(Vol. 157, Article 112288; DOI: 10.1016/j.nanoen.2026.112288).
2026.09.02 View 553 -
KAIST and Caltech Build Global Co-Mentoring Model for Next-Gen Researchers
KAIST is building a new global cooperation model with the Caltech — a world-renowned U.S. research institution — that goes beyond joint research to jointly nurture next-generation researchers as well.
KAIST, led by President Choongsik Bae, is holding the 1st KAIST-Caltech Joint Workshop on Molecular Science and Chemical Innovation with the Caltech in the United States from September 1 to 2.
The workshop is designed not merely to share the latest research achievements in advanced molecular science and future chemical technologies, but to build a sustainable framework of cooperation that links joint research and talent development, extending even to the shared use of research facilities.
At this workshop, particular attention is being given to establishing and operating the KAIST-Caltech Global Research Fellow (KCGRF) platform, a joint mentoring program for postdoctoral researchers.
KCGRF is a program in which faculty members from both institutions identify joint research topics and jointly select and mentor postdoctoral researchers. By enabling participating researchers to experience the research environments of both KAIST and Caltech, the program aims to nurture next-generation scientists with strong international research capabilities.
This joint initiative represents a concrete practice model of KAIST's Global Connect strategy. The vision goes beyond simple exchange with overseas universities. It aims to realize a collaborative internationalization, in which talent, knowledge, and research ideas flow between the two institutions, leading to joint research and the joint training of next-generation researchers.
Caltech is a world-renowned research institution with a long-standing tradition of excellence in the natural sciences, including chemistry and physics, and has produced numerous Nobel laureates. KAIST plans to combine Caltech's basic science research capabilities with KAIST's strengths in AI-driven and autonomous research to jointly pioneer new research topics in the field of future chemistry.
Collaboration between KAIST and Caltech began in 2024 through the BrainLink program, which supports exchanges among outstanding researchers. Centered on the Nitrogen-Hydrogen Synergy Hub Research Center, led by Professor Hyungjun Kim of the KAIST Department of Chemistry, the two institutions have continued joint research and researcher exchanges. This year, the partnership expanded further after being selected for the Ministry of Science and ICT’s Top-Tier Research Institution Cooperation Platform and Joint Research Support Program. Through this program, KAIST launched the Center for Intelligent Circular Chemical Ecosystem Innovation, led by Professor Sang Woo Han of the KAIST Department of Chemistry.
Building on this cooperation, the KAIST Department of Chemistry and Caltech’s Division of Chemistry and Chemical Engineering signed a memorandum of understanding (MOU) in March 2026, further strengthening their collaborative framework. The two institutions plan to hold joint workshops every year, match faculty members for joint mentoring, identify postdoctoral researchers, promote reciprocal research visits, and establish a sustainable operating structure for the joint mentoring program.
At the workshop, researchers are exploring new opportunities for collaboration in key fields shaping the future of chemistry, including AI for chemistry, autonomous laboratories, computational and theoretical chemistry, molecular science, catalysis, synthesis, electrochemistry, and sustainable and circular chemistry. KAIST is also pursuing plans to jointly utilize its autonomous laboratories in synthesis and electrochemistry with Caltech researchers. By jointly utilizing this advanced research environment, in which AI supports experimental design, execution, and data analysis, researchers from both institutions plan to expand the speed and scope of their joint research.
In addition, the two institutions plan to promote short-term reciprocal visits by graduate students, building a sustainable human and academic exchange system that connects graduate students, postdoctoral researchers, and faculty members. The vision goes beyond one-off, project-centered cooperation, aiming to build a long-term foundation in which next-generation researchers from both institutions naturally interact and create new joint research.
The Center for Intelligent Circular Chemical Ecosystem Innovation aims to build a new chemical technology platform that contributes to carbon neutrality and the circular economy by developing intelligent chemical upcycling technologies that convert chemical industry byproducts into high-value chemicals and materials.
Professor Sang Woo Han of KAIST's Department of Chemistry said, "Through this workshop, we aim to expand the KAIST-Caltech collaboration from joint research to a stage where we jointly nurture next-generation researchers." He added, "By connecting the strengths of both institutions, we will work to create new research topics and achievements in the field of future chemistry."
President Choongsik Bae of KAIST said, "KAIST's international cooperation is about building relationships in which outstanding universities, people, and knowledge from around the world flow back and forth and grow together." He emphasized, "By expanding collaboration with world-class research institutions such as Caltech, we will carry KAIST's 'Global Connect' forward into concrete achievements."
Professor Sarah Reisman from Caltech’s Division of Chemistry and Chemical Engineering, “Caltech and KAIST chemistry faculty have a long history of student exchanges and research collaboration, and this workshop is a wonderful opportunity to bring our faculty together and launch new projects supported by the Center for Intelligent Circular Chemical Ecosystem Innovation.” She added, “We are delighted to continue our strong partnership with KAIST.”
2026.09.02 View 427 -
KAIST Develops a Soft 3D-Printed Robotic Hand that Gently Grips Everything from Eggs to a 1 kg Water Bottle
3D printers that once could only produce rigid objects can now create products as soft and stretchable as rubber. A team of Korean researchers used AI to identify the optimal "recipe" for a material that can be printed into complex shapes while stretching to more than six times its original length. The material is expected to expand the range of applications for 3D printing, from robotic hands to form-fitting wearable devices and custom medical devices.
KAIST (President Choong-Sik Bae) announced on September 1 that a research team led by Professor Seungchul Lee from the Department of Mechanical Engineering, working with Dr. Jongbeom Na's team at the Korea Institute of Science and Technology’s (KIST, President Sang-Rok Oh) Extreme Materials Research Center and Professor Bumsoo Park from the Department of Manufacturing Systems and Design Engineering (MSDE) at Seoul National University of Science and Technology (SEOULTECH, President Dong-Hwan Kim), had used AI to develop a material that is both 3D-printable and highly stretchable, like rubber.
The need for such materials — soft, stretchable, and capable of forming complex shapes — has been growing as soft robots that come into direct contact with people, wearable devices worn on the body, and medical devices custom-fitted to patients have drawn increasing attention.
The 3D printing technology the team used, Digital Light Processing (DLP), cures a liquid material into a desired shape by exposing it to light. While DLP can quickly produce complex structures, making a material more stretchable and durable tends to raise its viscosity to the point that it no longer flows well enough to be printed. Conversely, thinning the material to make it easier to print reduces its stretchability and strength. Thus, developing a material that is both easy to print and highly stretchable was the central challenge.
The team used AI to identify the optimal "material recipe" that satisfies both conditions. Notably, the training data included not only materials that print well, but also highly viscous materials that are difficult to print.
The researchers cured various liquid material formulations in small molds and measured how stretchable and hard they were, how quickly they cured under light, and how well they flowed. This produced a dataset linking a wide range of material formulations to their respective properties.
The team then used machine learning to examine the relationship between material formulation and performance. Based on this, the AI identified the optimal material combination that is both 3D-printable and highly stretchable.
The material identified by the AI printed reliably on a DLP 3D printer and showed high stretchability, extending to more than six times its original length when pulled, without easily tearing.
To verify its real-world potential, the team 3D-printed a "soft actuator" using the material. A soft actuator is a device that uses air pressure and other means to create gentle, muscle-like movement. When inflated with air, it expanded like a balloon and bent as naturally as a human finger.
A soft robotic hand made by combining several actuators lifted a 1 kg water bottle and successfully and stably grasped objects of varying shapes and rigidity, from fragile eggs to glass bottles, an egg carton, and a computer mouse.
Beyond developing a single highly stretchable material, this research is significant for presenting an AI-based method for more quickly identifying materials with desired properties.
Previously, researchers had to directly formulate and test countless materials to find the optimal combination. Going forward, AI can first identify promising material combinations based on experimental data, which researchers then verify through testing, thereby reducing trial and error and shortening material development time.
"This research is significant as it shows that combining researchers' experimental data with artificial intelligence can efficiently identify optimal material combinations that were previously difficult to find," explained Professor Seungchul Lee. "We expect it to be used to more rapidly develop 3D-printing materials with the performance needed across a range of fields, including soft robots, wearable devices, and custom medical devices."
The study, with Dr. Younghan Song and Professor Bumsoo Park as co-first authors, was published in the international journal Nature Communications on June 4.
Paper title: Machine learning guided formulation design of digital light processing printable elastomers beyond viscosity stretchability tradeoff
DOI: https://doi.org/10.1038/s41467-026-73735-4
This research was supported by the Ministry of Trade, Industry and Resource's Machinery and Equipment Industry Technology Development Program (20023762), and by the Ministry of Science and ICT's Nano & Material Technology Development Program (RS-2026-25534767) and Excellent New Researcher Program (RS-2024-00350423).
2026.09.01 View 908