Kaist

KAIST Ushers in Era of Predicting ‘Optimal Alloys’..

  < 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.       < external_image >   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.”       < external_image >   <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.

A KAIST Team Engineers a Microbial Platform for Ef..

  <(From Left) Ph.D. Candidate Hyunmin Eun, Distinguished Professor Sang Yup Lee, , Dr. Cindy Pricilia Surya Prabowo>   The application of systems metabolic engineering strategies, along with the construction of an electron channeling system, has enabled the first gram-per-liter scale production of lutein from Corynebacterium glutamicum, providing a viable alternative to plant-derived lutein production.   A research group at KAIST has successfully engineered a microbial strain capable of producing lutein at industrially relevant levels. The team, led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, developed a novel C. glutamicum strain using systems metabolic engineering strategies to overcome the limitations of previous microbial lutein production efforts. This research is expected to be beneficial for the efficient production of other industrially important natural products used in food, pharmaceuticals, and cosmetics.   Lutein is a xanthophyll carotenoid found in egg yolk, fruits, and vegetables, known for its role in protecting our eyes from oxidative stress and reducing the risk of macular degeneration and cataracts. Currently, commercial lutein is predominantly extracted from marigold flowers; however, this approach has several drawbacks, including long cultivation times, high labor costs, and inefficient extraction yields, making it economically unfeasible for large-scale production. These challenges have driven the demand for alternative production methods.   To address these issues, KAIST researchers, including Ph.D. Candidate Hyunmin Eun, Dr. Cindy Pricilia Surya Prabowo, and Distinguished Professor Sang Yup Lee, applied systems metabolic engineering strategies to engineer C. glutamicum, a GRAS (Generally Recognized As Safe) microorganism widely used in industrial fermentation. Unlike Escherichia coli, which was previously explored for microbial lutein production, C. glutamicum lacks endotoxins, making it a safer and more viable option for food and pharmaceutical applications.   The team’s work, entitled “Gram-per-litre scale production of lutein by engineered Corynebacterium,” was published in Nature Synthesis on 04 July , 2025.   This research details the high-level production of lutein using glucose as a renewable carbon source via systems metabolic engineering. The team focused on eliminating metabolic bottlenecks that previously limited microbial lutein synthesis. By employing enzyme scaffold-based electron channeling strategies, the researchers improved metabolic flux towards lutein biosynthesis while minimizing unwanted byproducts.       <Lutein production metabolic pathway engineering>   To enhance productivity, bottleneck enzymes within the metabolic pathway were identified and optimized. It was determined that electron-requiring cytochrome P450 enzymes played a major role in limiting lutein biosynthesis. To overcome this limitation, an electron channeling strategy was implemented, where engineered cytochrome P450 enzymes and their reductase partners were spatially organized on synthetic scaffolds, allowing more efficient electron transfer and significantly increasing lutein production.   The engineered C. glutamicum strain was further optimized in fed-batch fermentation, achieving a record-breaking 1.78 g/L of lutein production within 54 hours, with a content of 19.51 mg/gDCW and a productivity of 32.88 mg/L/h—the highest lutein production performance in any host reported to date. This milestone demonstrates the feasibility of replacing plant-based lutein extraction with microbial fermentation technology.   “We can anticipate that this microbial cell factory-based mass production of lutein will be able to replace the current plant extraction-based process,” said Ph.D. Candidate Hyunmin Eun. He emphasized that the integrated metabolic engineering strategies developed in this study could be broadly applied for the efficient production of other valuable natural products used in pharmaceuticals and nutraceuticals.       <Schematic diagram of microbial-based lutein production platform>   “As maintaining good health in an aging society becomes increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other medically and nutritionally significant natural products,” added Distinguished Professor Sang Yup Lee.   This work is supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry project 2022M3J5A1056072 and the Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries project 2022M3J5A1056117 from the National Research Foundation supported by the Korean Ministry of Science and ICT.     Source: Hyunmin Eun (1st), Cindy Pricilia Surya Prabowo (co-1st), and Sang Yup Lee (Corresponding). “Gram-per-litre scale production of lutein by engineered Corynebacterium”. Nature Synthesis (Online published)     For further information: Sang Yup Lee, Distinguished Professor of Chemical and Biomolecular Engineering, KAIST (leesy＠kaist.ac.kr, Tel: ＋82-42-350-3930)

Professor Jung-woo＇ Choi ‘s Team Comes in First at..

  < Photo1. (From left) Ph.D candidate Yong-hoo Kwon, M.S candidate Do-hwan Kim, Professor Jung-woo Choi, Dr. Dong-heon Lee >   'Acoustic separation and classification technology' is a next-generation artificial intelligence (AI) core technology that enables the early detection of abnormal sounds in areas such as drones, fault detection of factory pipelines, and border surveillance systems, or allows for the separation and editing of spatial audio by sound source when producing AR/VR content.   On the 11th of July, a research team led by Professor Jung-woo Choi of KAIST's Department of Electrical and Electronic Engineering won first place in the 'Spatial Semantic Segmentation of Sound Scenes' task of the 'DCASE2025 Challenge,' the world's most prestigious acoustic detection and analysis competition.   This year’s challenge featured 86 teams competing across six tasks. In this competition, the KAIST research team achieved the best performance in their first-ever participation to Task 4. Professor Jung-woo Choi’s research team consisted of Dr. Dong-heon, Lee, Ph.D. candidate Young-hoo Kwon, and M.S. candidate Do-hwan Kim.   Task 4 titled 'Spatial Semantic Segmentation of Sound Scenes' is a highly demanding task requiring the analysis of spatial information in multi-channel audio signals with overlapping sound sources. The goal was to separate individual sounds and classify them into 18 predefined categories. The research team plans to present their technology at the DCASE workshop in Barcelona this October.       < external_image > <Figure 1. Example of an acoustic scene with multiple mixed sounds>   Early this year, Dr. Dong-heon Lee developed a state-of-the-art sound source separation AI that combines Transformer and Mamba architectures. During the competition, centered around researcher Young-hoo Kwon, they completed a ‘chain-of-inference architecture' AI model that performs sound source separation and classification again, using the waveforms and types of the initially separated sound sources as clues. This AI model is inspired by human’s auditory scene analysis mechanism that isolates individual sounds by focusing on incomplete clues such as sound type, rhythm, or direction, when listening to complex sounds.   Through this, the team was the only participant to achieve double-digit performance (11 dB) in 'Class-Aware Signal-to-Distortion Ratio Improvement (CA-SDRi)＊,' which is the measure for ranking how well the AI separated and classified sounds, proving their technical excellence.   Class-Aware Signal-to-Distortion Ratio Improvement (CA-SDRi): Measures how much clearer (less distorted) the desired sound is separated and classified compared to the original audio, in dB (decibels). A higher number indicates more accurate and cleaner sound separation. Prof. Jung-woo Choi remarked, "The research team has showcased world-leading acoustic separation AI models for the past three years, and I am delighted that these results have been officially recognized." He added, "I am proud of every member of the research team for winning first place through focused research, despite the significant increase in difficulty and having only a few weeks for development."       < external_image > <Figure 2. Time-frequency patterns of sound sources separated from a mixed source>   The IEEE DCASE Challenge 2025 was held online, with submissions accepted from April 1 to June 15 and results announced on June 30. Since its launch in 2013, the DCASE Challenge has served as a premier global platform of IEEE Signal Processing Society for showcasing cutting-edge AI models in acoustic signal processing.   This research was supported by the Mid-Career Researcher Support Project and STEAM Research Project of the National Research Foundation of Korea, funded by the Ministry of Education, Science and Technology, as well as support from the Future Defense Research Center, funded by the Defense Acquisition Program Administration and the Agency for Defense Development.

KAIST Presents a Breakthrough in Overcoming Drug R..

  < external_image > <(From the left) Prof. Hyun Uk Kim, Ph.D candiate Hae Deok Jung, Ph.D candidate Jina Lim, Prof.Yoosik Kim from the Department of Chemical and Biomolecular Engineering>   One of the biggest obstacles in cancer treatment is drug resistance in cancer cells. Conventional efforts have focused on identifying new drug targets to eliminate these resistant cells, but such approaches can often lead to even stronger resistance. Now, researchers at KAIST have developed a computational framework to predict key metabolic genes that can re-sensitize resistant cancer cells to treatment. This technique holds promise not only for a variety of cancer therapies but also for treating metabolic diseases such as diabetes.   On the 7th of July, KAIST (President Kwang Hyung Lee) announced that a research team led by Professors Hyun Uk Kim and Yoosik Kim from the Department of Chemical and Biomolecular Engineering had developed a computational framework that predicts metabolic gene targets to re-sensitize drug-resistant breast cancer cells. This was achieved using a metabolic network model capable of simulating human metabolism.   Focusing on metabolic alterations—key characteristics in the formation of drug resistance—the researchers developed a metabolism-based approach to identify gene targets that could enhance drug responsiveness by regulating the metabolism of drug-resistant breast cancer cells.       < external_image >   < Computational framework that can identify metabolic gene targets to revert the metabolic state of the drug-resistant cells to that of the drug-sensitive parental cells>   The team first constructed cell-specific metabolic network models by integrating proteomic data obtained from two different types of drug-resistant MCF7 breast cancer cell lines: one resistant to doxorubicin and the other to paclitaxel. They then performed gene knockout simulations＊ on all of the metabolic genes and analyzed the results. ＊Gene knockout simulation: A computational method to predict changes in a biological network by virtually removing specific genes.   As a result, they discovered that suppressing certain genes could make previously resistant cancer cells responsive to anticancer drugs again. Specifically, they identified GOT1 as a target in doxorubicin-resistant cells, GPI in paclitaxel-resistant cells, and SLC1A5 as a common target for both drugs.   The predictions were experimentally validated by suppressing proteins encoded by these genes, which led to the re-sensitization of the drug-resistant cancer cells.   Furthermore, consistent re-sensitization effects were also observed when the same proteins were inhibited in other types of breast cancer cells that had developed resistance to the same drugs.   Professor Yoosik Kim remarked, “Cellular metabolism plays a crucial role in various intractable diseases including infectious and degenerative conditions. This new technology, which predicts metabolic regulation switches, can serve as a foundational tool not only for treating drug-resistant breast cancer but also for a wide range of diseases that currently lack effective therapies.”   Professor Hyun Uk Kim, who led the study, emphasized, “The significance of this research lies in our ability to accurately predict key metabolic genes that can make resistant cancer cells responsive to treatment again—using only computer simulations and minimal experimental data. This framework can be widely applied to discover new therapeutic targets in various cancers and metabolic diseases.”   The study, in which Ph.D. candidates JinA Lim and Hae Deok Jung from KAIST participated as co-first authors, was published online on June 25 in Proceedings of the National Academy of Sciences (PNAS), a leading multidisciplinary journal that covers top-tier research in life sciences, physics, engineering, and social sciences.     ※ Title: Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets ※ DOI: https://doi.org/10.1073/pnas.2425384122 ※ Authors: JinA Lim (KAIST, co-first author), Hae Deok Jung (KAIST, co-first author), Han Suk Ryu (Seoul National University Hospital, corresponding author), Yoosik Kim (KAIST, corresponding author), Hyun Uk Kim (KAIST, corresponding author), and five others.   This research was supported by the Ministry of Science and ICT through the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute (ETRI).

KAIST Presents Game-Changing Technology for Intrac..

  < (From left)Professor Kyung Cheol Choi, Professor Hyunjoo J. Lee, Dr. Somin Lee from the School of Electrical Engineering >   Optogenetics is a technique that controls neural activity by stimulating neurons expressing light-sensitive proteins with specific wavelengths of light. It has opened new possibilities for identifying causes of brain disorders and developing treatments for intractable neurological diseases. Because this technology requires precise stimulation inside the human brain with minimal damage to soft brain tissue, it must be integrated into a neural probe—a medical device implanted in the brain. KAIST researchers have now proposed a new paradigm for neural probes by integrating micro OLEDs into thin, flexible, implantable medical devices.   KAIST (President Kwang Hyung Lee) announced on the 6th of July that professor Kyung Cheol Choi and professor Hyunjoo J. Lee from the School of Electrical Engineering have jointly succeeded in developing an optogenetic neural probe integrated with flexible micro OLEDs.   Optical fibers have been used for decades in optogenetic research to deliver light to deep brain regions from external light sources. Recently, research has focused on flexible optical fibers and ultra-miniaturized neural probes that integrate light sources for single-neuron stimulation.   The research team focused on micro OLEDs due to their high spatial resolution and flexibility, which allow for precise light delivery to small areas of neurons. This enables detailed brain circuit analysis while minimizing side effects and avoiding restrictions on animal movement. Moreover, micro OLEDs offer precise control of light wavelengths and support multi-site stimulation, making them suitable for studying complex brain functions.   However, the device's electrical properties degrade easily in the presence of moisture or water, which limited their use as implantable bioelectronics. Furthermore, optimizing the high-resolution integration process on thin, flexible probes remained a challenge.   To address this, the team enhanced the operational reliability of OLEDs in moist, oxygen-rich environments and minimized tissue damage during implantation. They patterned an ultrathin, flexible encapsulation layer＊ composed of aluminum oxide and parylene-C (Al₂O₃/parylene-C) at widths of 260–600 micrometers (μm) to maintain biocompatibility. ＊Encapsulation layer: A barrier that completely blocks oxygen and water molecules from the external environment, ensuring the longevity and reliability of the device.   When integrating the high-resolution micro OLEDs, the researchers also used parylene-C, the same biocompatible material as the encapsulation layer, to maintain flexibility and safety. To eliminate electrical interference between adjacent OLED pixels and spatially separate them, they introduced a pixel define layer (PDL), enabling the independent operation of eight micro OLEDs.   Furthermore, they precisely controlled the residual stress and thickness in the multilayer film structure of the device, ensuring its flexibility even in biological environments. This optimization allowed for probe insertion without bending or external shuttles or needles, minimizing mechanical stress during implantation.       < s-Conceptual diagram of a flexible neural probe for integrated optogenetics (Micro-OLED) >   As a result, the team developed a flexible neural probe with integrated micro OLEDs capable of emitting more than one milliwatt per square millimeter (mW/mm²) at 470 nanometers (nm), the optimal wavelength for activating channelrhodopsin-2. This is a significantly high light output for optogenetics and biomedical stimulation applications.   The ultrathin flexible encapsulation layer exhibited a low water vapor transmission rate of 2.66×10⁻⁵ g/m²/day, allowing the device to maintain functionality for over 10 years. The parylene-C-based barrier also demonstrated excellent performance in biological environments, successfully enabling the independent operation of the integrated OLEDs without electrical interference or bending issues.   Dr. Somin Lee, the lead author from Professor Choi’s lab, stated, “We focused on fine-tuning the integration process of highly flexible, high-resolution micro OLEDs onto thin flexible probes, enhancing their biocompatibility and application potential. This is the first reported development of such flexible OLEDs in a probe format and presents a new paradigm for using flexible OLEDs as implantable medical devices for monitoring and therapy.”   This study, with Dr. Somin Lee as the first author, was published online on March 26 in Advanced Functional Materials (IF 18.5), a leading international journal in the field of nanotechnology, and was selected as the cover article for the upcoming July issue.   ※ Title: Advanced Micro-OLED Integration on Thin and Flexible Polymer Neural Probes for Targeted Optogenetic Stimulation ※ DOI: https://doi.org/10.1002/adfm.202420758   The research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the Electronic Medicine Technology Development Program (Project title: Development of Core Source Technologies and In Vivo Validation for Brain Cognition and Emotion-Enhancing Light-Stimulating Electronic Medicine).

Development of Core NPU Technology to Improve Chat..

  Latest generative AI models such as OpenAI's ChatGPT-4 and Google's Gemini 2.5 require not only high memory bandwidth but also large memory capacity. This is why generative AI cloud operating companies like Microsoft and Google purchase hundreds of thousands of NVIDIA GPUs. As a solution to address the core challenges of building such high-performance AI infrastructure, Korean researchers have succeeded in developing an NPU (Neural Processing Unit)＊ core technology that improves the inference performance of generative AI models by an average of over 60％ while consuming approximately 44％ less power compared to the latest GPUs.   ＊NPU (Neural Processing Unit): An AI-specific semiconductor chip designed to rapidly process artificial neural networks.     On the 4th, Professor Jongse Park's research team from KAIST School of Computing, in collaboration with HyperAccel Inc. (a startup founded by Professor Joo-Young Kim from the School of Electrical Engineering), announced that they have developed a high-performance, low-power NPU (Neural Processing Unit) core technology specialized for generative AI clouds like ChatGPT.       The technology proposed by the research team has been accepted by the '2025 International Symposium on Computer Architecture (ISCA 2025)', a top-tier international conference in the field of computer architecture.   The key objective of this research is to improve the performance of large-scale generative AI services by lightweighting the inference process, while minimizing accuracy loss and solving memory bottleneck issues. This research is highly recognized for its integrated design of AI semiconductors and AI system software, which are key components of AI infrastructure.   While existing GPU-based AI infrastructure requires multiple GPU devices to meet high bandwidth and capacity demands, this technology enables the configuration of the same level of AI infrastructure using fewer NPU devices through KV cache quantization＊. KV cache accounts for most of the memory usage, thereby its quantization significantly reduces the cost of building generative AI clouds.   ＊KV Cache (Key-Value Cache) Quantization: Refers to reducing the data size in a type of temporary storage space used to improve performance when operating generative AI models (e.g., converting a 16-bit number to a 4-bit number reduces data size by 1/4).   The research team designed it to be integrated with memory interfaces without changing the operational logic of existing NPU architectures. This hardware architecture not only implements the proposed quantization algorithm but also adopts page-level memory management techniques＊ for efficient utilization of limited memory bandwidth and capacity, and introduces new encoding technique optimized for quantized KV cache.   ＊Page-level memory management technique: Virtualizes memory addresses, as the CPU does, to allow consistent access within the NPU.   Furthermore, when building an NPU-based AI cloud with superior cost and power efficiency compared to the latest GPUs, the high-performance, low-power nature of NPUs is expected to significantly reduce operating costs.   Professor Jongse Park stated, "This research, through joint work with HyperAccel Inc., found a solution in generative AI inference lightweighting algorithms and succeeded in developing a core NPU technology that can solve the 'memory problem.' Through this technology, we implemented an NPU with over 60％ improved performance compared to the latest GPUs by combining quantization techniques that reduce memory requirements while maintaining inference accuracy, and hardware designs optimized for this".       He further emphasized, "This technology has demonstrated the possibility of implementing high-performance, low-power infrastructure specialized for generative AI, and is expected to play a key role not only in AI cloud data centers but also in the AI transformation (AX) environment represented by dynamic, executable AI such as 'Agentic AI'."       This research was presented by Ph.D. student Minsu Kim and Dr. Seongmin Hong from HyperAccel Inc. as co-first authors at the '2025 International Symposium on Computer Architecture (ISCA)' held in Tokyo, Japan, from June 21 to June 25. ISCA, a globally renowned academic conference, received 570 paper submissions this year, with only 127 papers accepted (an acceptance rate of 22.7％).     ※Paper Title: Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization   ※DOI: https://doi.org/10.1145/3695053.3731019   Meanwhile, this research was supported by the National Research Foundation of Korea's Excellent Young Researcher Program, the Institute for Information & Communications Technology Planning & Evaluation (IITP), and the AI Semiconductor Graduate School Support Project.

KAIST researcher Se Jin Park develops ＇SpeechSSM,＇..

  < (From Left)Prof. Yong Man Ro and Ph.D. candidate Sejin Park >   Se Jin Park, a researcher from Professor Yong Man Ro’s team at KAIST, has announced 'SpeechSSM', a spoken language model capable of generating long-duration speech that sounds natural and remains consistent. An efficient processing technique based on linear sequence modeling overcomes the limitations of existing spoken language models, enabling high-quality speech generation without time constraints. It is expected to be widely used in podcasts, audiobooks, and voice assistants due to its ability to generate natural, long-duration speech like humans.   Recently, Spoken Language Models (SLMs) have been spotlighted as next-generation technology that surpasses the limitations of text-based language models by learning human speech without text to understand and generate linguistic and non-linguistic information. However, existing models showed significant limitations in generating long-duration content required for podcasts, audiobooks, and voice assistants. Now, KAIST researcher has succeeded in overcoming these limitations by developing 'SpeechSSM,' which enables consistent and natural speech generation without time constraints.   KAIST(President Kwang Hyung Lee) announced on the 3rd of July that Ph.D. candidate Sejin Park from Professor Yong Man Ro's research team in the School of Electrical Engineering has developed 'SpeechSSM,' a spoken. a spoken language model capable of generating long-duration speech.   This research is set to be presented as an oral paper at ICML (International Conference on Machine Learning) 2025, one of the top machine learning conferences, selected among approximately 1％ of all submitted papers. This not only proves outstanding research ability but also serves as an opportunity to once again demonstrate KAIST's world-leading AI research capabilities.   A major advantage of Spoken Language Models (SLMs) is their ability to directly process speech without intermediate text conversion, leveraging the unique acoustic characteristics of human speakers, allowing for the rapid generation of high-quality speech even in large-scale models.       However, existing models faced difficulties in maintaining semantic and speaker consistency for long-duration speech due to increased 'speech token resolution' and memory consumption when capturing very detailed information by breaking down speech into fine fragments.   To solve this problem, Se Jin Park developed 'SpeechSSM,' a spoken language model using a Hybrid State-Space Model, designed to efficiently process and generate long speech sequences.   This model employs a 'hybrid structure' that alternately places 'attention layers' focusing on recent information and 'recurrent layers' that remember the overall narrative flow (long-term context). This allows the story to flow smoothly without losing coherence even when generating speech for a long time. Furthermore, memory usage and computational load do not increase sharply with input length, enabling stable and efficient learning and the generation of long-duration speech.   SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech.   Additionally, in the speech generation phase, it uses a 'Non-Autoregressive' audio synthesis model (SoundStorm), which rapidly generates multiple parts at once instead of slowly creating one character or one word at a time, enabling the fast generation of high-quality speech.   While existing models typically evaluated short speech models of about 10 seconds, Se Jin Park created new evaluation tasks for speech generation based on their self-built benchmark dataset, 'LibriSpeech-Long,' capable of generating up to 16 minutes of speech.   Compared to PPL (Perplexity), an existing speech model evaluation metric that only indicates grammatical correctness, she proposed new evaluation metrics such as 'SC-L (semantic coherence over time)' to assess content coherence over time, and 'N-MOS-T (naturalness mean opinion score over time)' to evaluate naturalness over time, enabling more effective and precise evaluation.       < external_image >   Through these new evaluations, it was confirmed that speech generated by the SpeechSSM spoken language model consistently featured specific individuals mentioned in the initial prompt, and new characters and events unfolded naturally and contextually consistently, despite long-duration generation. This contrasts sharply with existing models, which tended to easily lose their topic and exhibit repetition during long-duration generation.       < external_image >   PhD candidate Sejin Park explained, "Existing spoken language models had limitations in long-duration generation, so our goal was to develop a spoken language model capable of generating long-duration speech for actual human use." She added, "This research achievement is expected to greatly contribute to various types of voice content creation and voice AI fields like voice assistants, by maintaining consistent content in long contexts and responding more efficiently and quickly in real time than existing methods."   This research, with Se Jin Park as the first author, was conducted in collaboration with Google DeepMind and is scheduled to be presented as an oral presentation at ICML (International Conference on Machine Learning) 2025 on July 16th.   Paper Title: Long-Form Speech Generation with Spoken Language Models DOI: 10.48550/arXiv.2412.18603   Ph.D. candidate Se Jin Park has demonstrated outstanding research capabilities as a member of Professor Yong Man Ro's MLLM (multimodal large language model) research team, through her work integrating vision, speech, and language. Her achievements include a spotlight paper presentation at 2024 CVPR (Computer Vision and Pattern Recognition) and an Outstanding Paper Award at 2024 ACL (Association for Computational Linguistics).   For more information, you can refer to the publication and accompanying demo: SpeechSSM Publications.

KAIST Uses AI to Discover Optimal New Material for..

  < (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.

KAIST Develops AI to Easily Find Promising Materia..

  < Photo 1. (From left) Professor Jihan Kim, Ph.D. candidate Yunsung Lim and Dr. Hyunsoo Park of the Department of Chemical and Biomolecular Engineering >   In order to help prevent the climate crisis, actively reducing already-emitted CO₂ is essential. Accordingly, direct air capture (DAC) — a technology that directly extracts only CO₂ from the air — is gaining attention. However, effectively capturing pure CO₂ is not easy due to water vapor (H₂O) present in the air. KAIST researchers have successfully used AI-driven machine learning techniques to identify the most promising CO₂-capturing materials among metal-organic frameworks (MOFs), a key class of materials studied for this technology.   KAIST (President Kwang Hyung Lee) announced on the 29th of June that a research team led by Professor Jihan Kim from the Department of Chemical and Biomolecular Engineering, in collaboration with a team at Imperial College London, has developed a machine-learning-based simulation method that can quickly and accurately screen MOFs best suited for atmospheric CO₂ capture.       < Figure 1. Concept diagram of Direct Air Capture (DAC) technology and carbon capture using Metal-Organic Frameworks (MOFs). MOFs are promising porous materials capable of capturing carbon dioxide from the atmosphere, drawing attention as a core material for DAC technology. >   To overcome the difficulty of discovering high-performance materials due to the complexity of structures and the limitations of predicting intermolecular interactions, the research team developed a machine learning force field (MLFF) capable of precisely predicting the interactions between CO₂, water (H₂O), and MOFs. This new method enables calculations of MOF adsorption properties with quantum-mechanics-level accuracy at vastly faster speeds than before.   Using this system, the team screened over 8,000 experimentally synthesized MOF structures, identifying more than 100 promising candidates for CO₂ capture. Notably, this included new candidates that had not been uncovered by traditional force-field-based simulations. The team also analyzed the relationships between MOF chemical structure and adsorption performance, proposing seven key chemical features that will help in designing new materials for DAC.       < Figure 2. Concept diagram of adsorption simulation using Machine Learning Force Field (MLFF). The developed MLFF is applicable to various MOF structures and allows for precise calculation of adsorption properties by predicting interaction energies during repetitive Widom insertion simulations. It is characterized by simultaneously achieving high accuracy and low computational cost compared to conventional classical force fields. >   This research is recognized as a significant advance in the DAC field, greatly enhancing materials design and simulation by precisely predicting MOF-CO₂ and MOF-H₂O interactions.   The results of this research, with Ph.D. candidate Yunsung Lim and Dr. Hyunsoo Park of KAIST as co-first authors, were published in the international academic journal Matter on June 12.   ※Paper Title: Accelerating CO₂ direct air capture screening for metal–organic frameworks with a transferable machine learning force field   ※DOI: 10.1016/j.matt.2025.102203   This research was supported by the Saudi Aramco-KAIST CO₂ Management Center and the Ministry of Science and ICT's Global C.L.E.A.N. Project.

KAIST＇s Li-Fi - Achieves 100 Times Faster Speed an..

- KAIST-KRISS Develop 'On-Device Encryption Optical Transmitter' Based on Eco-Friendly Quantum Dots   - New Li-Fi Platform Technology Achieves High Performance with 17.4％ Device Efficiency and 29,000 nit Brightness, Simultaneously Improving Transmission Speed and Security   - Presents New Methodology for High-Speed and Encrypted Communication Through Single-Device-Based Dual-Channel Optical Modulation       < Photo 1. (Front row from left) Seungmin Shin, First Author; Professor Himchan Cho; (Back row from left) Hyungdoh Lee, Seungwoo Lee, Wonbeom Lee; (Top left) Dr. Kyung-geun Lim >   Li-Fi (Light Fidelity) is a wireless communication technology that utilizes the visible light spectrum (400-800 THz), similar to LED light, offering speeds up to 100 times faster than existing Wi-Fi (up to 224 Gbps). While it has fewer limitations in available frequency allocation and less radio interference, it is relatively vulnerable to security breaches as anyone can access it. Korean researchers have now proposed a new Li-Fi platform that overcomes the limitations of conventional optical communication devices and can simultaneously enhance both transmission speed and security.   KAIST (President Kwang Hyung Lee) announced on the 24th that Professor Himchan Cho's research team from the Department of Materials Science and Engineering, in collaboration with Dr. Kyung-geun Lim of the Korea Research Institute of Standards and Science (KRISS, President Ho-Seong Lee) under the National Research Council of Science & Technology (NST, Chairman Young-Sik Kim), has developed 'on-device encryption optical communication device' technology for the utilization of 'Li-Fi,' which is attracting attention as a next-generation ultra-high-speed data communication.   Professor Cho's team created high-efficiency light-emitting triode devices using eco-friendly quantum dots (low-toxicity and sustainable materials). The device developed by the research team is a mechanism that generates light using an electric field. Specifically, the electric field is concentrated in 'tiny holes (pinholes) in the permeable electrode' and transmitted beyond the electrode. This device utilizes this principle to simultaneously process two input data streams.   Using this principle, the research team developed a technology called 'on-device encryption optical transmitter.' The core of this technology is that the device itself converts information into light and simultaneously encrypts it. This means that enhanced security data transmission is possible without the need for complex, separate equipment.   External Quantum Efficiency (EQE) is an indicator of how efficiently electricity is converted into light, with a general commercialization standard of about 20％. The newly developed device recorded an EQE of 17.4％, and its luminance was 29,000 nit, significantly exceeding the maximum brightness of a smartphone OLED screen, which is 2,000 nit, demonstrating a brightness more than 10 times higher.       < Figure 1. Schematic diagram of the device structure developed by the research team and encrypted communication >   Furthermore, to more accurately understand how this device converts information into light, the research team used a method called 'transient electroluminescence analysis.' They analyzed the light-emitting characteristics generated by the device when voltage was instantaneously applied for very short durations (hundreds of nanoseconds = billionths of a second). Through this analysis, they investigated the movement of charges within the device at hundreds of nanoseconds, elucidating the operating mechanism of dual-channel optical modulation implemented within a single device.   Professor Himchan Cho of KAIST stated, "This research overcomes the limitations of existing optical communication devices and proposes a new communication platform that can both increase transmission speed and enhance security."       < Photo 2. Professor Himchan Cho, Department of Materials Science and Engineering >   He added, "This technology, which strengthens security without additional equipment and simultaneously enables encryption and transmission, can be widely applied in various fields where security is crucial in the future."   This research, with Seungmin Shin, a Ph.D. candidate at KAIST's Department of Materials Science and Engineering, participating as the first author, and Professor Himchan Cho and Dr. Kyung-geun Lim of KRISS as co-corresponding authors, was published in the international journal 'Advanced Materials' on May 30th and was selected as an inside front cover paper.     ※ Paper Title: High-Efficiency Quantum Dot Permeable electrode Light-Emitting Triodes for Visible-Light Communications and On-Device Data Encryption   ※ DOI: https://doi.org/10.1002/adma.202503189   This research was supported by the National Research Foundation of Korea, the National Research Council of Science & Technology (NST), and the Korea Institute for Advancement of Technology.

KAIST Develops Glare-Free, Heat-Blocking ＇Smart Wi..

• Professor Hong Chul Moon of the Department of Chemical and Biomolecular Engineering develops RECM, a next-generation smart window technology, expecting cooling energy savings and effective indoor thermal management.   • When using the developed RECM, a significantly superior temperature reduction effect is observed compared to conventional windows.   • With a 'pedestrian-friendly smart window' design that eliminates glare by suppressing external reflections, it is expected to be adapted in architectural structures, transportation, and more.       < (From left) First author Hoy Jung Jo, Professor Hong Chul Moon >   In the building sector, which accounts for approximately 40％ of global energy consumption, heat ingress through windows has been identified as a primary cause of wasted heating and cooling energy. Our research team has successfully developed a 'pedestrian-friendly smart window' technology capable of not only reducing heating and cooling energy in urban buildings but also resolving the persistent issue of 'light pollution' in urban living.   On the 17th of June, Professor Hong Chul Moon's research team at KAIST's Department of Chemical and Biomolecular Engineering announced the development of a 'smart window technology' that allows users to control the light and heat entering through windows according to their intent, and effectively neutralize glare from external sources.   Recently, 'active smart window' technology, which enables free adjustment of light and heat based on user operation, has garnered significant attention. Unlike conventional windows that passively react to changes in temperature or light, this is a next-generation window system that can be controlled in real-time via electrical signals.   The next-generation smart window technology developed by the research team, RECM (Reversible Electrodeposition and Electrochromic Mirror), is a smart window system based on a single-structured ＊electrochromic device that can actively control the transmittance of visible light and near-infrared (heat).   ＊Electrochromic device: A device whose optical properties change in response to an electrical signal.   In particular, by effectively suppressing the glare phenomenon caused by external reflected light—a problem previously identified in traditional metal ＊deposition smart windows—through the combined application of electrochromic materials, a 'pedestrian-friendly smart window' suitable for building facades has been realized.   ＊Deposition: A process involving the electrochemical reaction to coat metal ions, such as Ag＋, onto an electrode surface in solid form.   The RECM system developed in this study operates in three modes depending on voltage control.   Mode I (Transparent Mode) is advantageous for allowing sunlight to enter the indoor space during winter, as it transmits both light and heat like ordinary glass.   In Mode II (Colored Mode), ＊Prussian Blue (PB) and ＊＊DHV＋• chemical species are formed through a redox (oxidation-reduction) reaction, causing the window to turn a deep blue color. In this state, light is absorbed, and only a portion of the heat is transmitted, allowing for privacy while enabling appropriate indoor temperature control.   ＊Prussian Blue: An electrochromic material that transitions between colorless and blue upon electrical stimulation.   ＊＊DHV＋•: A radical state colored molecule generated upon electrical stimulation.   Mode III (Colored and Deposition Mode) involves the reduction and deposition of silver (Ag＋) ions on the electrode surface, reflecting both light and heat. Concurrently, the colored material absorbs the reflected light, effectively blocking glare for external pedestrians.   The research team validated the practical indoor temperature reduction effect of the RECM technology through experiments utilizing a miniature model house. When a conventional glass window was installed, the indoor temperature rose to 58.7°C within 45 minutes. Conversely, when RECM was operated in Mode III, the temperature reached 31.5°C, demonstrating a temperature reduction effect of approximately 27.2°C.   Furthermore, since each state transition is achievable solely by electrical signals, it is regarded as an active smart technology capable of instantaneous response according to season, time, and intended use.       < Figure 1. Operation mechanism of the RECM smart window. The RECM system can switch among three states—transparent, colored, and colored & deposition—via electrical stimulation. At -1.6 V, DHV•＋ and Prussian Blue (PB) are formed, blocking visible light to provide privacy protection and heat blocking. At -2.0 V, silver (Ag) is deposited on the electrode surface, reflecting light and heat, while DHV•＋ and Prussian Blue absorb reflected light, effectively suppressing external glare. Through this mechanism, it functions as an active smart window that simultaneously controls light, heat, and glare. >   Professor Hong Chul Moon of KAIST, the corresponding author of this study, stated, "This research goes beyond existing smart window technologies limited to visible light control, presenting a truly smart window platform that comprehensively considers not only active indoor thermal control but also the visual safety of pedestrians." He added, "Various applications are anticipated, from urban buildings to vehicles and trains."       < Figure 2. Analysis of glare suppression effect of conventional reflective smart windows and RECM. This figure presents the results comparing the glare phenomenon occurring during silver (Ag) deposition between conventional reflective smart windows and RECM Mode III. Conventional reflective devices resulted in strong reflected light on the desk surface due to their high reflectivity. In contrast, RECM Mode III, where the colored material absorbed reflected light, showed a 33％ reduction in reflected light intensity, and no reflected light was observed from outside. This highlights the RECM system's distinctiveness and practicality as a 'pedestrian-friendly smart window' optimized for dense urban environments, extending beyond just heat blocking. >   The findings of this research were published on June 13, 2025, in Volume 10, Issue 6 of 'ACS Energy Letters'. The listed authors for this publication are Hoy Jung Jo, Yeon Jae Jang, Hyeon-Don Kim, Kwang-Seop Kim, and Hong Chul Moon.   ※ Paper Title: Glare-Free, Energy-Efficient Smart Windows: A Pedestrian-Friendly System with Dynamically Tunable Light and Heat Regulation   ※ DOI: 10.1021/acsenergylett.5c00637       < Figure 3. Temperature reduction performance verification in a miniature model house. The actual heat blocking effect was evaluated by applying RECM devices to a model building. Under identical conditions, the indoor temperature with ordinary glass rose to 58.7°C, whereas with RECM in Mode III, it reached 31.5°C, demonstrating a maximum temperature reduction effect of 27.2°C. The indoor temperature difference was also visually confirmed through thermal images, which proves the potential for indoor temperature control in urban buildings. >   This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and the internal research program of the Korea Institute of Machinery and Materials. 

High-Resolution Spectrometer that Fits into Smartp..

- Professor Mooseok Jang's research team at the Department of Bio and Brain Engineering develops an ultra-compact, high-resolution spectrometer using 'double-layer disordered metasurfaces' that generate unique random patterns depending on light's color.   - Unlike conventional dispersion-based spectrometers that were difficult to apply to portable devices, this new concept spectrometer technology achieves 1nm-level high resolution in a device smaller than 1cm, comparable in size to a fingernail.   - It can be utilized as a built-in spectrometer in smartphones and wearable devices in the future, and can be expanded to advanced optical technologies such as hyperspectral imaging and ultrafast imaging.       < Photo 1. (From left) Professor Mooseok Jang, Dong-gu Lee (Ph.D. candidate), Gookho Song (Ph.D. candidate) >   Color, as the way light's wavelength is perceived by the human eye, goes beyond a simple aesthetic element, containing important scientific information like a substance's composition or state. Spectrometers are optical devices that analyze material properties by decomposing light into its constituent wavelengths, and they are widely used in various scientific and industrial fields, including material analysis, chemical component detection, and life science research. Existing high-resolution spectrometers were large and complex, making them difficult for widespread daily use. However, thanks to the ultra-compact, high-resolution spectrometer developed by KAIST researchers, it is now expected that light's color information can be utilized even within smartphones or wearable devices.   KAIST (President Kwang Hyung Lee) announced on the 13th that Professor Mooseok Jang's research team at the Department of Bio and Brain Engineering has successfully developed a reconstruction-based spectrometer technology using double-layer disordered metasurfaces＊.   ＊Double-layer disordered metasurface: An innovative optical device that complexly scatters light through two layers of disordered nanostructures, creating unique and predictable speckle patterns for each wavelength.   Existing high-resolution spectrometers have a large form factor, on the order of tens of centimeters, and require complex calibration processes to maintain accuracy. This fundamentally stems from the operating principle of traditional dispersive elements, such as gratings and prisms, which separate light wavelengths along the propagation direction, much like a rainbow separates colors. Consequently, despite the potential for light's color information to be widely useful in daily life, spectroscopic technology has been limited to laboratory or industrial manufacturing environments.       < Figure 1. Through a simple structure consisting of a double layer of disordered metasurfaces and an image sensor, it was shown that speckles of predictable spectral channels with high spectral resolution can be generated in a compact form factor. The high similarity between the measured and calculated speckles was used to solve the inverse problem and verify the ability to reconstruct the spectrum. >   The research team devised a method that departs from the conventional spectroscopic paradigm of using diffraction gratings or prisms, which establish a one-to-one correspondence between light's color information and its propagation direction, by utilizing designed disordered structures as optical components. In this process, they employed metasurfaces, which can freely control the light propagation process using structures tens to hundreds of nanometers in size, to accurately implement 'complex random patterns (speckle＊)'.   ＊Speckle: An irregular pattern of light intensity created by the interference of multiple wavefronts of light.   Specifically, they developed a method that involves implementing a double-layer disordered metasurface to generate wavelength-specific speckle patterns and then reconstructing precise color information (wavelength) of the light from the random patterns measured by a camera.   As a result, they successfully developed a new concept spectrometer technology that can accurately measure light across a broad range of visible to infrared (440-1,300nm) with a high resolution of 1 nanometer (nm) in a device smaller than a fingernail (less than 1cm) using only a single image capture.       < Figure 2. A disordered metasurface is a metasurface with irregularly arranged structures ranging from tens to hundreds of nanometers in size. In a double-layer structure, a propagation space is placed between the two metasurfaces to control the output speckle with high degrees of freedom, thereby achieving a spectral resolution of 1 nm even in a form factor smaller than 1 cm. >   Dong-gu Lee, a lead author of this study, stated, "This technology is implemented in a way that is directly integrated with commercial image sensors, and we expect that it will enable easy acquisition and utilization of light's wavelength information in daily life when built into mobile devices in the future."   Professor Mooseok Jang said, "This technology overcomes the limitations of existing RGB three-color based machine vision fields, which only distinguish and recognize three color components (red, green, blue), and has diverse applications. We anticipate various applied research for this technology, which expands the horizon of laboratory-level technology to daily-level machine vision technology for applications such as food component analysis, crop health diagnosis, skin health measurement, environmental pollution detection, and bio/medical diagnostics." He added, "Furthermore, it can be extended to various advanced optical technologies such as hyperspectral imaging, which records wavelength and spatial information simultaneously with high resolution, 3D optical trapping technology, which precisely controls light of multiple wavelengths into desired forms, and ultrafast imaging technology, which captures phenomena occurring in very short periods."   This research was collaboratively led by Dong-gu Lee (Ph.D. candidate) and Gookho Song (Ph.D. candidate) from the KAIST Department of Bio and Brain Engineering as co-first authors, with Professor Mooseok Jang as the corresponding author. The findings were published online in the international journal Science Advances on May 28, 2025.   ＊ Paper Title: Reconstructive spectrometer using double-layer disordered metasurfaces ＊ DOI: 10.1126/sciadv.adv2376   This research was supported by the Samsung Research Funding and Incubation Center of Samsung Electronics grant, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), and the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT).