Intel
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KAIST Proposes AI Training Method that will Drastically Shorten Time for Complex Quantum Mechanical Calculations
- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded for the first time in accelerating quantum mechanical electronic structure calculations using a convolutional neural network (CNN) model
- Presenting an AI learning principle of quantum mechanical 3D chemical bonding information, the work is expected to accelerate the computer-assisted designing of next-generation materials and devices
The close relationship between AI and high-performance scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for their AI-related research contributions in their respective fields of study. KAIST researchers succeeded in dramatically reducing the computation time for highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel AI approach.
KAIST (President Kwang-Hyung Lee) announced on the 30th of October that Professor Yong-Hoon Kim's team from the School of Electrical Engineering developed a 3D computer vision artificial neural network-based computation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations traditionally performed using supercomputers to derive the properties of materials.
< Figure 1. Various methodologies are utilized in the simulation of materials and materials, such as quantum mechanical calculations at the nanometer (nm) level, classical mechanical force fields at the scale of tens to hundreds of nanometers, continuum dynamics calculations at the macroscopic scale, and calculations that mix simulations at different scales. These simulations are already playing a key role in a wide range of basic research and application development fields in combination with informatics techniques. Recently, there have been active efforts to introduce machine learning techniques to radically accelerate simulations, but research on introducing machine learning techniques to quantum mechanical electronic structure calculations, which form the basis of high-scale simulations, is still insufficient. >
The quantum mechanical density functional theory (DFT) calculations using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow fast and accurate prediction of material properties.
*Density functional theory (DFT): A representative theory of ab initio (first principles) calculations that calculate quantum mechanical properties from the atomic level.
However, practical DFT calculations require generating 3D electron density and solving quantum mechanical equations through a complex, iterative self-consistent field (SCF)* process that must be repeated tens to hundreds of times. This restricts its application to systems with only a few hundred to a few thousand atoms.
*Self-consistent field (SCF): A scientific computing method widely used to solve complex many-body problems that must be described by a number of interconnected simultaneous differential equations.
Professor Yong-Hoon Kim’s research team questioned whether recent advancements in AI techniques could be used to bypass the SCF process. As a result, they developed the DeepSCF model, which accelerates calculations by learning chemical bonding information distributed in a 3D space using neural network algorithms from the field of computer vision.
< Figure 2. The deepSCF methodology developed in this study provides a way to rapidly accelerate DFT calculations by avoiding the self-consistent field process (orange box) that had to be performed repeatedly in traditional quantum mechanical electronic structure calculations through artificial neural network techniques (green box). The self-consistent field process is a process of predicting the 3D electron density, constructing the corresponding potential, and then solving the quantum mechanical Cohn-Sham equations, repeating tens to hundreds of times. The core idea of the deepSCF methodology is that the residual electron density (δρ), which is the difference between the electron density (ρ) and the sum of the electron densities of the constituent atoms (ρ0), corresponds to chemical bonding information, so the self-consistent field process is replaced with a 3D convolutional neural network model. >
The research team focused on the fact that, according to density functional theory, electron density contains all quantum mechanical information of electrons, and that the residual electron density — the difference between the total electron density and the sum of the electron densities of the constituent atoms — contains chemical bonding information. They used this as the target for machine learning.
They then adopted a dataset of organic molecules with various chemical bonding characteristics, and applied random rotations and deformations to the atomic structures of these molecules to further enhance the model’s accuracy and generalization capabilities. Ultimately, the research team demonstrated the validity and efficiency of the DeepSCF methodology on large, complex systems.
< Figure 3. An example of applying the deepSCF methodology to a carbon nanotube-based DNA sequence analysis device model (top left). In addition to classical mechanical interatomic forces (bottom right), the residual electron density (top right) and quantum mechanical electronic structure properties such as the electronic density of states (DOS) (bottom left) containing information on chemical bonding are rapidly predicted with an accuracy corresponding to the standard DFT calculation results that perform the SCF process. >
Professor Yong-Hoon Kim, who supervised the research, explained that his team had found a way to map quantum mechanical chemical bonding information in a 3D space onto artificial neural networks. He noted, “Since quantum mechanical electron structure calculations underpin materials simulations across all scales, this research establishes a foundational principle for accelerating material calculations using artificial intelligence.”
Ryong-Gyu Lee, a PhD candidate in the School of Electrical Engineering, served as the first author of this research, which was published online on October 24 in Npj Computational Materials, a prestigious journal in the field of material computation. (Paper title: “Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints”)
This research was conducted with support from the KAIST High-Risk Research Program for Graduate Students and the National Research Foundation of Korea’s Mid-career Researcher Support Program.
2024.10.30 View 3700 -
KAIST Develops Technology for the Precise Diagnosis of Electric Vehicle Batteries Using Small Currents
Accurately diagnosing the state of electric vehicle (EV) batteries is essential for their efficient management and safe use. KAIST researchers have developed a new technology that can diagnose and monitor the state of batteries with high precision using only small amounts of current, which is expected to maximize the batteries’ long-term stability and efficiency.
KAIST (represented by President Kwang Hyung Lee) announced on the 17th of October that a research team led by Professors Kyeongha Kwon and Sang-Gug Lee from the School of Electrical Engineering had developed electrochemical impedance spectroscopy (EIS) technology that can be used to improve the stability and performance of high-capacity batteries in electric vehicles.
EIS is a powerful tool that measures the impedance* magnitude and changes in a battery, allowing the evaluation of battery efficiency and loss. It is considered an important tool for assessing the state of charge (SOC) and state of health (SOH) of batteries. Additionally, it can be used to identify thermal characteristics, chemical/physical changes, predict battery life, and determine the causes of failures. *Battery Impedance: A measure of the resistance to current flow within the battery that is used to assess battery performance and condition.
However, traditional EIS equipment is expensive and complex, making it difficult to install, operate, and maintain. Moreover, due to sensitivity and precision limitations, applying current disturbances of several amperes (A) to a battery can cause significant electrical stress, increasing the risk of battery failure or fire and making it difficult to use in practice.
< Figure 1. Flow chart for diagnosis and prevention of unexpected combustion via the use of the electrochemical impedance spectroscopy (EIS) for the batteries for electric vehicles. >
To address this, the KAIST research team developed and validated a low-current EIS system for diagnosing the condition and health of high-capacity EV batteries. This EIS system can precisely measure battery impedance with low current disturbances (10mA), minimizing thermal effects and safety issues during the measurement process.
In addition, the system minimizes bulky and costly components, making it easy to integrate into vehicles. The system was proven effective in identifying the electrochemical properties of batteries under various operating conditions, including different temperatures and SOC levels.
Professor Kyeongha Kwon (the corresponding author) explained, “This system can be easily integrated into the battery management system (BMS) of electric vehicles and has demonstrated high measurement accuracy while significantly reducing the cost and complexity compared to traditional high-current EIS methods. It can contribute to battery diagnosis and performance improvements not only for electric vehicles but also for energy storage systems (ESS).”
This research, in which Young-Nam Lee, a doctoral student in the School of Electrical Engineering at KAIST participated as the first author, was published in the prestigious international journal IEEE Transactions on Industrial Electronics (top 2% in the field; IF 7.5) on September 5th. (Paper Title: Small-Perturbation Electrochemical Impedance Spectroscopy System With High Accuracy for High-Capacity Batteries in Electric Vehicles, Link: https://ieeexplore.ieee.org/document/10666864)
< Figure 2. Impedance measurement results of large-capacity batteries for electric vehicles. ZEW (commercial EW; MP10, Wonatech) versus ZMEAS (proposed system) >
This research was supported by the Basic Research Program of the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Program of the Korea Evaluation Institute of Industrial Technology, and the AI Semiconductor Graduate Program of the Institute of Information & Communications Technology Planning & Evaluation.
2024.10.17 View 4097 -
A heated battle of science and sports, who is the winner of this year's KA-PO War?
< Photos from KAIST-POSTECH Science War (photographed by Student Junhyeok Park of KAIST Freshman Course) >
The future leaders of science at KAIST and POSTECH (President Seong Keun Kim) held their annual science and sporting event at POSTECH for two days from September 20th to 21st. The 'KAIST-POSTECH Science War (hereafter KA-PO War)' is a festival consisting of science and sports games and various side events to promote exchange and cooperation between the two universities. It is also known by the nickname 'Science War'.
KA-PO War consists of △Science Games △e-Sports △Athletics, and the two universities compete in a total of 7 events including hacking competitions, artificial intelligence programming (AI), science quizzes, League of Legends (LOL), baseball, basketball, and soccer. In particular, the 9-hour ‘hacking competition’ and the ‘AI programming’ competition, which pits the AI design strategies of the two universities against each other, are famous for its competitions that are not easily seen at other universities.
The future science leaders of KAIST and POSTECH competed with their brains and physical strength even in the rain, and in the competition where the university that wins more than 4 out of 7 events wins, KAIST won with a score of 6 to 1 after fierce matches.
In addition, for this KAIST competition, Byeong-cheol Kim, the CEO of POSTECH Holdings and an alumnus of the Department of Industrial Management at POSTECH, donated 10 million won for the preparation of this event.
< Photos from KA-PO War site (photographed by Student Junhyeok Park of KAIST Freshman Course) >
KA-PO War Director Henry Kwon (KAIST Department of Electrical and Electronic Engineering) said, “I would like to thank the planning team and supporters who worked hard to make it a successful event. This year’s KA-PO War shined even brighter because of the students from both universities who cheered passionately and played games despite the hot weather and rain. I hope this will be an opportunity to further strengthen the bond and sense of belonging among engineering students.”
KA-PO War Preparatory Committee Chairman Sa-joon Hong (POSTECH Department of Physics) said, “As if to manifest this year’s motto, ‘BLAST,’ the intense heat swept through the competition, and regardless of the outcome, the students from both universities took away unforgettable and precious memories.”
As a kind of student festival jointly held between the two universities, which have been held annually since 2002, KAIST-POSTECH Science Wars is held under a different name each year depending on the venue. This year, it was held at POSTECH, thus called ‘KA-PO War.’
2024.09.19 View 2096 -
KAIST and NYU set out to Install Korea's First Joint Degree Program in AI
< (From left) New York University President Linda Mills and President Kwang-Hyung Lee >
KAIST (President Kwang-Hyung Lee) and New York University (NYU, President Linda G. Mills) signed an MOU in the afternoon of the 9th to introduce a graduate program for a joint degree in the field of artificial intelligence.
This agreement was promoted based on the consensus between the two universities that strengthening capabilities in the field of AI and fostering global talent are essential elements that can lead to great development in the entire future society beyond simple technical education.
The two universities have been operating joint research groups in various industrial fields related to AI and convergence with it, and based on this agreement, they plan to establish an operating committee within this year to design a joint degree program for graduate school courses related to artificial intelligence.
A KAIST official said, “If the joint degree program in AI is implemented, it is expected to be an unprecedented innovative experiment in which KAIST and NYU join forces to create ‘a single AI degree.’
The committee will consist of an equal number of faculty members from both schools, and will discuss the overall strategic planning of the joint degree program, including ▴curriculum structure and course composition ▴course completion roadmap ▴calculation of faculty and student population ▴calculation of budget size ▴calculation of operating facility size and details ▴legal matters regarding certification. In addition, the development of a new logo symbolizing the joint degree of KAIST and NYU in AI will also be carried out.
The two schools expect that the joint degree program being promoted this time will contribute to advancing education and research capabilities in the field of artificial intelligence, jointly discovering and fostering talent in related fields that are currently lacking worldwide, and will become an exemplary case of global education and research cooperation.
The faculty members of both schools, who possess excellent capabilities, will provide innovative and creative education in the field of artificial intelligence. Students will receive support to gain top-level research experience by participating in various international joint research projects promoted by the faculty members of both schools. Through this, the core of this joint degree program promoted by both schools is to continuously cultivate excellent human resources who will lead the future global society.
Since signing a cooperation agreement for the establishment of a joint campus in June 2022, KAIST and NYU have been promoting campus sharing, joint research, and joint bachelor's degree programs. Including this, they are developing an innovative joint campus model and establishing an active international cooperation model.
In particular, the exchange student system for undergraduate students will be implemented starting from the second semester of the 2023 academic year. 30 students from KAIST and 11 students from NYU were selected through a competitive selection process and are participating. In the case of KAIST students, if they complete one of the six minor programs at NYU, they will receive a degree that states the completion of the minor upon graduation.
Based on the performance of the undergraduate exchange student operation, the two schools have also agreed to introduce a dual degree system for master's and doctoral students, and specific procedures are currently in progress.
In addition, from 2023 to the present, we are carrying out future joint research projects in 15 fields that are integrated with AI, and we plan to begin international joint research in 10 fields centered on AI and bio from the fourth quarter of this year.
NYU President Linda Mills said, “AI technology can play a significant role in addressing various social challenges such as climate change, health care, and education inequality,” and added that, “The global talent cultivated through our two schools will also go on to make innovative contributions to solving these social problems.”
Kwang-Hyung Lee, the president of KAIST, said, “In the era of competition for global hegemony in technology, the development of AI technology is an essential element for countries and companies to secure competitiveness,” and “Through long-term cooperation with NYU, we will take the lead in fostering world-class, advanced talents who can innovatively apply and develop AI in various fields.”
The signing ceremony held at the Four Seasons Hotel in Seoul was attended by KAIST officials including President Kwang-Hyung Lee, Hyun Deok Yeo, the Director of G-School, NYU officials including President Linda Mills, Kyunghyun Cho, a Professor of Computer Science and Data Science, and Dr. Karin Pavese, the Executive Director of NYU-KAIST Innovation Research Institute, amid attendance by other key figures from the industries situated in Korea. (End)
2024.09.10 View 4355 -
KAIST presents strategies for Holotomography in advanced bio research
Measuring and analyzing three-dimensional (3D) images of live cells and tissues is considered crucial in advanced fields of biology and medicine. Organoids, which are 3D structures that mimic organs, are particular examples that significantly benefits 3D live imaging. Organoids provide effective alternatives to animal testing in the drug development processes, and can rapidly determine personalized medicine. On the other hand, active researches are ongoing to utilize organoids for organ replacement.
< Figure 1. Schematic illustration of holotomography compared to X-ray CT. Similar to CT, they share the commonality of measuring the optical properties of an unlabeled specimen in three dimensions. Instead of X-rays, holotomography irradiates light in the visible range, and provides refractive index measurements of transparent specimens rather than absorptivity. While CT obtains three-dimensional information only through mechanical rotation of the irradiating light, holotomography can replace this by applying wavefront control technology in the visible range. >
Organelle-level observation of 3D biological specimens such as organoids and stem cell colonies without staining or preprocessing holds significant implications for both innovating basic research and bioindustrial applications related to regenerative medicine and bioindustrial applications.
Holotomography (HT) is a 3D optical microscopy that implements 3D reconstruction analogous to that of X-ray computed tomography (CT). Although HT and CT share a similar theoretical background, HT facilitates high-resolution examination inside cells and tissues, instead of the human body. HT obtains 3D images of cells and tissues at the organelle level without chemical or genetic labeling, thus overcomes various challenges of existing methods in bio research and industry. Its potential is highlighted in research fields where sample physiology must not be disrupted, such as regenerative medicine, personalized medicine, and infertility treatment.
< Figure 2. Label-free 3D imaging of diverse live cells. Time-lapse image of Hep3B cells illustrating subcellular morphology changes upon H2O2 treatment, followed by cellular recovery after returning to the regular cell culture medium. >
This paper introduces the advantages and broad applicability of HT to biomedical researchers, while presenting an overview of principles and future technical challenges to optical researchers. It showcases various cases of applying HT in studies such as 3D biology, regenerative medicine, and cancer research, as well as suggesting future optical development. Also, it categorizes HT based on the light source, to describe the principles, limitations, and improvements of each category in detail. Particularly, the paper addresses strategies for deepening cell and organoid studies by introducing artificial intelligence (AI) to HT.
Due to its potential to drive advanced bioindustry, HT is attracting interest and investment from universities and corporates worldwide. The KAIST research team has been leading this international field by developing core technologies and carrying out key application researches throughout the last decade.
< Figure 3. Various types of cells and organelles that make up the imaging barrier of a living intestinal organoid can be observed using holotomography. >
This paper, co-authored by Dr. Geon Kim from KAIST Research Center for Natural Sciences, Professor Ki-Jun Yoon's team from the Department of Biological Sciences, Director Bon-Kyoung Koo's team from the Institute for Basic Science (IBS) Center for Genome Engineering, and Dr. Seongsoo Lee's team from the Korea Basic Science Institute (KBSI), was published in 'Nature Reviews Methods Primers' on the 25th of July. This research was supported by the Leader Grant and Basic Science Research Program of the National Research Foundation, the Hologram Core Technology Development Grant of the Ministry of Science and ICT, the Nano and Material Technology Development Project, and the Health and Medical R&D Project of the Ministry of Health and Welfare.
2024.07.30 View 3869 -
KAIST Employs Image-recognition AI to Determine Battery Composition and Conditions
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.
KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.
*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.
The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.
The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.
< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >
The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.
Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."
Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."
< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >
This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)
2024.07.02 View 4818 -
KAIST President Kwang-Hyung Lee receives honorary doctorate from Université de Montréal
KAIST (President Kwang-Hyung Lee) announced on June 16th that President Kwang-Hyung Lee received an honorary doctorate on the 15th, local time, from the Université de Montréal in Canada, one of the largest French-speaking universities in North America.
< Image. (from left) Mr. Pierre Lassonde, Chairman of the Board of Polytechnique Montréal, President Maud Cohen of Polytechnique Montréal, President Kwang-Hyung Lee of KAIST, Chancellor Frantz Saintellemy of Université de Montréal and Mr. Alexandre Chabot, Secretary General of Université de Montéal. >
President Lee was selected as the recipient of the honorary doctorate from the Université de Montréal upon the recommendation of Polytechnique Montréal in recognition of his contributions in pioneering the multidisciplinary approach to integrate a number of fields studies including computer science, biology, and nanotechnology.
Polytechnique Montréal is a university in affiliation with the University of Montréal and is one of the largest engineering education and research institutions of Canada. President Lee's honorary doctorate was awarded at the Convocation Ceremony of Polytechnique Montréal held for the Class of 2024. On this day, Mr. Serge Gendron, a businessman, a philanthropist and an alum of Polytechnique Montréal, also had the honor of receiving an honorary doctorate along with President Lee.
President Kwang-Hyung Lee is internationally recognized for his contributions in various fields, including engineering education, multidisciplinary research, strategy establishment, and future prospects. President Lee is also well known to have had significant influence on the first-generation venture entrepreneurs, a large portion of which are from KAIST, who have now grown into full-fledged entrepreneurs. For these activities, President Lee received numerous decorations and commendations within Korea, including the National Order of Civil Merit - Camellia Medal, and in 2003, he received the ‘Légion d’Honneur Chevalier’ from the French government as well.
Through his speech at the ceremony, KAIST President Kwang-Hyung Lee expressed his gratitude to the Université de Montréal and Polytechnique Montréal, while congratulating and encouraging the graduates who are poised to start anew as they part from the school. “Hold on to your dreams, try looking at the world from a different perspective, and enjoy the challenges without being afraid of failures.” With these three pieces of advice, President Lee cheered on the graduates saying, “The future belongs to those of you who challenge them.”
Maud Cohen, the President of Polytechnique Montréal, commented on President Kwang-Hyung Lee's honorary doctorate, that Polytechnique Montréal is proud to award an honorary doctorate to Mr. Lee for his exceptional career path, his holistic, multidisciplinary and undeniably forward-looking vision, which strongly echoes the values of Polytechnique Montréal, and for his involvement in and commitment to education, research and the future of the next generation.
* Established in 1873, Polytechnique Montréal is one of Canada’s largest engineering education and research universities, and is located on the Université de Montréal campus – North America’s largest Francophone university campus. Joshua Bengio, who won the Turing Award for establishing the foundations of deep learning, is gaining international recognition in artificial intelligence and other related fields at Polytechnique Montréal. Polytechnique Montréal chose KAIST as the first Korean university establish partnership with and has continued to build up close cooperative relationship since 1998.
* The Université de Montréal (UdeM) is a public university founded in 1878. It is located in Montréal, in the French-speaking province of Québec, Canada. It is one of Canada's five major universities, and the second largest in terms of student enrollment. The Université de Montréal is the largest in the French-speaking world in terms of both student enrollment and research. The Université de Montréal enjoys an excellent reputation as one of the best French-language post-secondary institutions. Its rector is Mr. Daniel Jutras.
2024.06.16 View 5031 -
KAIST appoints K-Pop Star “G-Dragon” Kwon Ji-yong as a visiting professor
KAIST (President Kwang Hyung Lee) announced on June 5th that its Department of Mechanical Engineering has appointed Kwon Ji-yong, a K-Pop star better known as G-Dragon, as a visiting professor. The appointment was officiated at the “Innovate Korea 2024” event held at the KAIST Sports Complex in the morning of the 5th.
This appointment was promoted to expand the global competitiveness of Korean culture by applying the latest scientific technology developed from KAIST to K-content and the cultural industry.
An official from Galaxy Corporation, Mr. Kwon’s agency, said, “Through this appointment, we expect to contribute to market innovation and to the global spread and growth of K-culture through research and development of ‘entertech’*.”
* Entertech: A compound word of ‘entertainment’ and ‘technology’ refering to an industry that creates new added value by combining ICT with intellectual property rights (IP) and contents, which are the core values of entertainment.
The newly appointed Visiting Professor Kwon Ji-yong is scheduled to give a special lecture on leadership to undergraduate and graduate students. The purpose is to share his experience and life as a world-class artist to provide KAIST students with vision and insight into the world, as well as the courage and inspiration to pioneer within their own fields of expertise.
In addition, cultural events are planned to be held to help facilitate communication with KAIST members and provide artistic experiences necessary to grow into creative and convergent talents.
Joint research that applies KAIST's various technologies to art and cultural contents will also be pursued. ‘(temporarily named) KAIST-Galaxy Corporation Entertech Research Center’ will be established within the Department of Mechanical Engineering, conducting ▴research on Digital Twin technology for Korean Wave artists, starting with G-Dragon himself ▴convergent research on science and technology encompassing artificial intelligence, robots, and metaverse and K-Culture and ▴research on the development of differentiated avatars of artists using the latest technologies such as volumetrics, motion capture, and haptics.
Galaxy Corporation produces highly talked about televised shows including 'Physical: 100 Season 2', which achieved the feat of ranking first in the Netflix’s ‘Global Top 10 Non-English TV Shows’ category, as well as 'Street Woman Fighter', '2 Days & 1 Night', 'Let's Play Soccer: Mungchyeoya Chanda' and 'Mr. Trot 2', and expanded its IP (intellectual property rights) to all aspects of entertainment, from broadcasting to the music industry by recruiting G-Dragon in December last year.
It is an 'entertech' company that is pioneering a new content market by combining IP with the latest digital technologies such as metaverse, avatar, and artificial intelligence (AI). Based on these achievements, it recently signed a partnership with a global investment bank (IB). Through global investment, the company is soon to be the first entertech startup to become a unicorn (corporate value of 1 trillion won).
Kwon Ji-yong said, “It is an honor to be a visiting professor at KAIST, where countless scientific geniuses come out of. I am hoping the best of science and technology experts and my expertise in the entertainment business will come together and a great synergy, a ‘big bang’ will be ensue as a result.
“More and more musicians are working with artificial intelligence and these cutting-edge technologies enable more diverse forms of creative work,” he continued. He also expressed his specific wish. “Through the AI avatar developed jointly by Galaxy Corporation and KAIST, I wish to communicate more closely with my fans around the world that I cannot meet so often otherwise.”
President Kwang-Hyung Lee said, “Considering that KAIST is a university that has always explored new things and pioneered unknown territories since its establishment, I believe that Kwon Ji-yong also shares KAIST's DNA in that he is a leader and pioneer who has attained world-class achievements in the world of culture and arts.”
President Lee continued expressing his expectations saying, “Kwon Ji-yong will not only contribute to taking KAIST’s science and technology to spread and grow K-culture on the global stage, through his activities but also in sharing his experience and spirit as the trend leader of the world, he will be an asset to the members of KAIST which aims to become a top-tier university.”
The period of Kwon Ji-yong’s professorship is two years from the 4th of this month to June of 2026. In addition, Professor Kwon Ji-yong has been appointed as KAIST’s global ambassador to help strengthen KAIST's international presence.
Professor Kwon Ji-yong, who began his career as G-Dragon, has established himself as an icon symbolizing Korean pop culture over 18 years as a member and the leader of the K-Pop idol group 'Big Bang', which debuted in 2006 and gained global popularity. While being recognized for his outstanding ability in all aspects of his music, from writing lyrics, composing, and producing to performing, he has also been active in fashion, becoming the first Asian man to be selected as a global ambassador for Chanel in 2016, spreading Korean music and fashion to the world.
In June, 2017, he released his 3rd solo album 'Kwon Ji Yong' on USB instead of the existing CD format, for the first time in Korea. He also attended CES, the world's largest home appliance and IT exhibition held in Las Vegas, USA in January of this year, extending his strides as a 'tech-tainer'.
2024.06.05 View 15941 -
KAIST researchers developed a novel ultra-low power memory for neuromorphic computing
A team of Korean researchers is making headlines by developing a new memory device that can be used to replace existing memory or used in implementing neuromorphic computing for next-generation artificial intelligence hardware for its low processing costs and its ultra-low power consumption.
KAIST (President Kwang-Hyung Lee) announced on April 4th that Professor Shinhyun Choi's research team in the School of Electrical Engineering has developed a next-generation phase change memory* device featuring ultra-low-power consumption that can replace DRAM and NAND flash memory.
☞ Phase change memory: A memory device that stores and/or processes information by changing the crystalline states of materials to be amorphous or crystalline using heat, thereby changing its resistance state.
Existing phase change memory has the problems such as expensive fabrication process for making highly scaled device and requiring substantial amount of power for operation. To solve these problems, Professor Choi’s research team developed an ultra-low power phase change memory device by electrically forming a very small nanometer (nm) scale phase changeable filament without expensive fabrication processes. This new development has the groundbreaking advantage of not only having a very low processing cost but also of enabling operating with ultra-low power consumption.
DRAM, one of the most popularly used memory, is very fast, but has volatile characteristics in which data disappears when the power is turned off. NAND flash memory, a storage device, has relatively slow read/write speeds, but it has non-volatile characteristic that enables it to preserve the data even when the power is cut off.
Phase change memory, on the other hand, combines the advantages of both DRAM and NAND flash memory, offering high speed and non-volatile characteristics. For this reason, phase change memory is being highlighted as the next-generation memory that can replace existing memory, and is being actively researched as a memory technology or neuromorphic computing technology that mimics the human brain.
However, conventional phase change memory devices require a substantial amount of power to operate, making it difficult to make practical large-capacity memory products or realize a neuromorphic computing system. In order to maximize the thermal efficiency for memory device operation, previous research efforts focused on reducing the power consumption by shrinking the physical size of the device through the use of the state-of-the-art lithography technologies, but they were met with limitations in terms of practicality as the degree of improvement in power consumption was minimal whereas the cost and the difficulty of fabrication increased with each improvement.
In order to solve the power consumption problem of phase change memory, Professor Shinhyun Choi’s research team created a method to electrically form phase change materials in extremely small area, successfully implementing an ultra-low-power phase change memory device that consumes 15 times less power than a conventional phase change memory device fabricated with the expensive lithography tool.
< Figure 1. Illustrations of the ultra-low power phase change memory device developed through this study and the comparison of power consumption by the newly developed phase change memory device compared to conventional phase change memory devices. >
Professor Shinhyun Choi expressed strong confidence in how this research will span out in the future in the new field of research saying, "The phase change memory device we have developed is significant as it offers a novel approach to solve the lingering problems in producing a memory device at a greatly improved manufacturing cost and energy efficiency. We expect the results of our study to become the foundation of future electronic engineering, enabling various applications including high-density three-dimensional vertical memory and neuromorphic computing systems as it opened up the possibilities to choose from a variety of materials.” He went on to add, “I would like to thank the National Research Foundation of Korea and the National NanoFab Center for supporting this research.”
This study, in which See-On Park, a student of MS-PhD Integrated Program, and Seokman Hong, a doctoral student of the School of Electrical Engineering at KAIST, participated as first authors, was published on April 4 in the April issue of the renowned international academic journal Nature. (Paper title: Phase-Change Memory via a Phase-Changeable Self-Confined Nano-Filament)
This research was conducted with support from the Next-Generation Intelligent Semiconductor Technology Development Project, PIM AI Semiconductor Core Technology Development (Device) Project, Excellent Emerging Research Program of the National Research Foundation of Korea, and the Semiconductor Process-based Nanomedical Devices Development Project of the National NanoFab Center.
2024.04.04 View 6707 -
The World’s First Hacking-preventing Cryptographic Semiconductor Chip
With the dramatic increase in the amount of information exchanged between components or devices in the 5G/6G era, such as for the Internet of Things (IoT) and autonomous driving, hacking attacks are becoming more sophisticated. Consequently, enhancing security functions is essential for safely transmitting data between and among devices.
On February 29th, a KAIST research team led by Professors Yang-gyu Choi and Seung-tak Ryu from the School of Electrical Engineering announced the successful development of the world's first security cryptographic semiconductor.
The team has developed the Cryptoristor, a cryptographic transistor based on FinFET technology, produced through a 100% silicon-compatible process, for the first time in the world. Cryptoristor is a random number generator (RNG) with unparalleled characteristics, featuring a unique structure comprising a single transistor and a distinctive mechanism.
In all security environments, including artificial intelligence, the most crucial element is the RNG. In the most commonly used security chip, the Advanced Encryption Standard (AES), the RNG is a core component, occupying approximately 75% of the total chip area and more than 85% of its energy consumption. Hence, there is an urgent need for the development of low-power/ultra-small RNGs suitable for mobile or IoT devices.
Existing RNGs come with limitations as they lack compatibility with silicon CMOS processes and circuit-based RNGs occupy a large surface area.
In contrast, the team’s newly developed Cryptoristor, a cryptographic semiconductor based on a single-component structure, consumes and occupies less than .001 of the power and area compared to the current chips being used. Utilizing the inherent randomness of FinFETs, fabricated on a Silicon-on-Insulator (SOI) substrate with an insulating layer formed beneath the silicon, the team developed an RNG that unpredictably produces zeroes and ones.
< Figure 1. Conceptual diagram of the security cryptographic transistor device. >
Generally speaking, preventing hackers from predicting the encrypted algorithms during data exchanges through mobile devices is pivotal. Therefore, this method ensures unpredictability by generating random sequences of zeroes and ones that change every time.
Moreover, while the Cryptoristor-based RNG research is the world's first of its kind without any international implementation cases, it shares the same transistor structure as existing logic or memory components. This enables 100% production through rapid mass production processes using existing semiconductor facilities at a low cost.
Seung-il Kim, a PhD student who led the research, explained the significance of the study, stating, "As a cryptographic semiconductor, the ultra-small/low-power random number generator enhances security through its distinctive unpredictability, supporting safe hyperconnectivity with secure transmissions between chips or devices. Particularly, compared to previous research, it offers excellent advantages in terms of energy consumption, integration density, and cost, making it suitable for IoT device environments."
This research, with master’s student Hyung-jin Yoo as the co-author, was officially published in the online edition of Science Advances, a sister journal of Science, in February 2024 (research paper title: Cryptographic transistor for true random number generator with low power consumption).
This research received support from the Next-Generation Intelligent Semiconductor Technology Development Project and the Core Technology Development Project for the National Semiconductor Research Laboratory.
2024.03.07 View 6681 -
KAIST Research Team Creates the Scent of Jasmine from Microorganisms
The fragrance of jasmine and ylang-ylang, used widely in the manufacturing of cosmetics, foods, and beverages, can be produced by direct extraction from their respective flowers. In reality, this makes it difficult for production to meet demand, so companies use benzyl acetate, a major aromatic component of the two fragrances that is chemically synthesized from raw materials derived from petroleum.
On February 26, a KAIST research team led by Research Professor Kyeong Rok Choi from the BioProcess Engineering Research Center and Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering revealed the development of the first microbial process to effectively produce benzyl acetate, an industrially useful compound, from renewable carbon sources such as glucose. The results were published in their paper titled “A microbial process for the production of benzyl acetate”.
< Figure 1. Production of benzyl acetate through co-culture of upstream and downstream strains harboring the benzoic acid-dependent pathway. >
The team, led by Distinguished Professor Lee, aimed to produce benzyl acetate through an environmentally friendly and sustainable method, and developed an Escherichia coli strand to convert glucose into benzyl acetate through system metabolic engineering*.
*System metabolic engineering: a field of research founded by Distinguished Professor Lee to effectively develop microbial cell plants, a core component of the bio-industry that will replace the existing chemical industry, which is highly dependent on petroleum.
The research team developed a metabolic pathway that biosynthesizes benzyl acetate from benzoic acid derived from glucose, and successfully produced benzyl acetate by co-culturing** the strain.
**co-culture: simultaneously synthesizing two or more types of microorganisms in a mixture
However, it has been confirmed that the enzyme used to convert benzoic acid into benzyl acetate in this co-culturing technique acts non-specifically on an intermediate product during benzoic acid biosynthesis, producing a by-product called cinnamyl acetate. This process consumes the intermediate product needed for benzoic acid biosynthesis, thereby reducing the production efficiency of the target compound, benzyl acetate.
To overcome this problem, Distinguished Professor Lee and his team devised a delayed co-culture method, where they first produced benzoic acid in the earlier stages of fermentation by only culturing the top strain that produces benzoic acid from glucose, and later inoculated the bottom strain to convert the accumulated benzoic acid in the culture medium into benzyl acetate.
By applying this co-culture technique, the team suppressed the formation of by-products without further strain improvement or applying additional enzymes, and multiplied the concentration of the target compound by 10 times, producing 2.2 g/L of benzyl acetate. In addition, the team confirmed its potential for the commercial production of benzyl acetate through a technical economic analysis on this microbial process.
< Figure 2. Delayed co-culture of the Bn1 and Bn-BnAc3 strains for improved production of benzyl acetate through the benzoic acid-independent pathway.>
Research Professor Keyong Rok Choi, who was the first author of this paper, said, “This work is significant in that we have developed an effective microbial process to produce the industrially useful compound benzyl acetate, and also in that we have suggested a new approach to overcome the target chemical efficiency diminution and by-product formation issues caused commonly through non-specific enzyme activities during metabolic engineering.”
Distinguished Professor Lee said, “If we can increase the variety and number of microbial processes that produce useful chemicals through sustainable methods and at the same time develop effective strategies to solve chronic and inevitable problems that arise during microbial strain development, we will be able to accelerate the transition from the petrochemical industry into the eco-friendly and sustainable bio-industry.
This work was published online in Nature Chemical Engineering, issued by Nature.
This research was supported by the ‘Implementation of Intelligent Cell Factory Technology (PI: Distinguished Professor Sang Yup Lee) Project by the Ministry of Science and ICT, and the ‘Development of Protein Production Technology from Inorganic Substances through Microbiological Metabolic System Control’ (PI: Research Professor Kyeong Rok Choi) by the Agricultural Microbiological Project Group at the Rural Development Administration.
2024.03.05 View 5638 -
KAIST Team Develops an Insect-Mimicking Semiconductor to Detect Motion
The recent development of an “intelligent sensor” semiconductor that mimics the optic nerve of insects while operating at ultra-high speeds and low power offers extensive expandability into various innovative technologies. This technology is expected to be applied to various fields including transportation, safety, and security systems, contributing to both industry and society.
On February 19, a KAIST research team led by Professor Kyung Min Kim from the Department of Materials Science and Engineering (DMSE) announced the successful developed an intelligent motion detector by merging various memristor* devices to mimic the visual intelligence** of the optic nerve of insects.
*Memristor: a “memory resistor” whose state of resistance changes depending on the input signal
**Visual intelligence: the ability to interpret visual information and perform calculations within the optic nerve
With the recent advances in AI technology, vision systems are being improved by utilizing AI in various tasks such as image recognition, object detection, and motion analysis. However, existing vision systems typically recognize objects and their behaviour from the received image signals using complex algorithms. This method requires a significant amount of data traffic and higher power consumption, making it difficult to apply in mobile or IoT devices.
Meanwhile, insects are known to be able to effectively process visual information through an optic nerve circuit called the elementary motion detector, allowing them to detect objects and recognize their motion at an advanced level. However, mimicking this pathway using conventional silicon integrated circuit (CMOS) technology requires complex circuits, and its implementation into actual devices has thus been limited.
< Figure 1. Working principle of a biological elementary motion detection system. >
Professor Kyung Min Kim’s research team developed an intelligent motion detecting sensor that operates at a high level of efficiency and ultra-high speeds. The device has a simple structure consisting of only two types of memristors and a resistor developed by the team. The two different memristors each carry out a signal delay function and a signal integration and ignition function, respectively. Through them, the team could directly mimic the optic nerve of insects to analyze object movement.
< Figure 2. (Left) Optical image of the M-EMD device in the left panel (scale bar 200 μm) and SEM image of the device in the right panel (scale bar: 20 μm). (Middle) Responses of the M-EMD in positive direction. (Right) Responses of the M-EMD in negative direction. >
To demonstrate its potential for practical applications, the research team used the newly developed motion detector to design a neuromorphic computing system that can predict the path of a vehicle. The results showed that the device used 92.9% less energy compared to existing technology and predicted motion with more accuracy.
< Figure 3. Neuromorphic computing system configuration based on motion recognition devices >
Professor Kim said, “Insects make use of their very simple visual intelligence systems to detect the motion of objects at a surprising high speed. This research is significant in that we could mimic the functions of a nerve using a memristor device.” He added, “Edge AI devices, such as AI-topped mobile phones, are becoming increasingly important. This research can contribute to the integration of efficient vision systems for motion recognition, so we expect it to be applied to various fields such as autonomous vehicles, vehicle transportation systems, robotics, and machine vision.”
This research, conducted by co-first authors Hanchan Song and Min Gu Lee, both Ph.D. candidates at KAIST DMSE, was published in the online issue of Advanced Materials on January 29.
This research was supported by the Mid-Sized Research Project by the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Project, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the National Nano Fab Center, and the Leap Research Project by KAIST.
2024.02.29 View 6165