AI
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KAIST Proposes AI Training Method that will Drastically Shorten Time for Calculations in Quantum Mechanics
- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded in accelerating calculations for electronic structure in quantum mechanics for the first time in the world using a convolutional neural network (CNN) model
- Presenting the learning principle of quantum mechanical 3D chemical bonding information through AI, expected to be applied to next-generation material and device computer design fields
The close relationship between AI and highly complicated scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for devising the AI for their respective fields of study. KAIST researchers succeeded in dramatically reducing the computation time for highly sophisticated computer simulations for quantum mechanics by predicting atomic-level chemical bonding information distributed in 3D space using a novel approach to teach AI.
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 density functional theory (DFT) calculations in quantum mechanics 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 for fast and accurate prediction of quantum 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, applying 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 property simulations at 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 Venture Research Program for Graduate and PhD Students and the National Research Foundation of Korea’s Mid-career Researcher Support Program.
2024.10.30 View 149 -
KAIST Proposes a New Way to Circumvent a Long-time Frustration in Neural Computing
The human brain begins learning through spontaneous random activities even before it receives sensory information from the external world. The technology developed by the KAIST research team enables much faster and more accurate learning when exposed to actual data by pre-learning random information in a brain-mimicking artificial neural network, and is expected to be a breakthrough in the development of brain-based artificial intelligence and neuromorphic computing technology in the future.
KAIST (President Kwang-Hyung Lee) announced on the 23rd of October that Professor Se-Bum Paik 's research team in the Department of Brain Cognitive Sciences solved the weight transport problem*, a long-standing challenge in neural network learning, and through this, explained the principles that enable resource-efficient learning in biological brain neural networks.
*Weight transport problem: This is the biggest obstacle to the development of artificial intelligence that mimics the biological brain. It is the fundamental reason why large-scale memory and computational work are required in the learning of general artificial neural networks, unlike biological brains.
Over the past several decades, the development of artificial intelligence has been based on error backpropagation learning proposed by Geoffery Hinton, who won the Nobel Prize in Physics this year. However, error backpropagation learning was thought to be impossible in biological brains because it requires the unrealistic assumption that individual neurons must know all the connected information across multiple layers in order to calculate the error signal for learning.
< Figure 1. Illustration depicting the method of random noise training and its effects >
This difficult problem, called the weight transport problem, was raised by Francis Crick, who won the Nobel Prize in Physiology or Medicine for the discovery of the structure of DNA, after the error backpropagation learning was proposed by Hinton in 1986. Since then, it has been considered the reason why the operating principles of natural neural networks and artificial neural networks will forever be fundamentally different.
At the borderline of artificial intelligence and neuroscience, researchers including Hinton have continued to attempt to create biologically plausible models that can implement the learning principles of the brain by solving the weight transport problem.
In 2016, a joint research team from Oxford University and DeepMind in the UK first proposed the concept of error backpropagation learning being possible without weight transport, drawing attention from the academic world. However, biologically plausible error backpropagation learning without weight transport was inefficient, with slow learning speeds and low accuracy, making it difficult to apply in reality.
KAIST research team noted that the biological brain begins learning through internal spontaneous random neural activity even before experiencing external sensory experiences. To mimic this, the research team pre-trained a biologically plausible neural network without weight transport with meaningless random information (random noise).
As a result, they showed that the symmetry of the forward and backward neural cell connections of the neural network, which is an essential condition for error backpropagation learning, can be created. In other words, learning without weight transport is possible through random pre-training.
< Figure 2. Illustration depicting the meta-learning effect of random noise training >
The research team revealed that learning random information before learning actual data has the property of meta-learning, which is ‘learning how to learn.’ It was shown that neural networks that pre-learned random noise perform much faster and more accurate learning when exposed to actual data, and can achieve high learning efficiency without weight transport.
< Figure 3. Illustration depicting research on understanding the brain's operating principles through artificial neural networks >
Professor Se-Bum Paik said, “It breaks the conventional understanding of existing machine learning that only data learning is important, and provides a new perspective that focuses on the neuroscience principles of creating appropriate conditions before learning,” and added, “It is significant in that it solves important problems in artificial neural network learning through clues from developmental neuroscience, and at the same time provides insight into the brain’s learning principles through artificial neural network models.”
This study, in which Jeonghwan Cheon, a Master’s candidate of KAIST Department of Brain and Cognitive Sciences participated as the first author and Professor Sang Wan Lee of the same department as a co-author, will be presented at the 38th Neural Information Processing Systems (NeurIPS), the world's top artificial intelligence conference, to be held in Vancouver, Canada from December 10 to 15, 2024. (Paper title: Pretraining with random noise for fast and robust learning without weight transport)
This study was conducted with the support of the National Research Foundation of Korea's Basic Research Program in Science and Engineering, the Information and Communications Technology Planning and Evaluation Institute's Talent Development Program, and the KAIST Singularity Professor Program.
2024.10.23 View 331 -
KAIST Develops a Fire-risk Free Self-Powered Hydrogen Production System
KAIST researchers have developed a new hydrogen production system that overcomes the current limitations of green hydrogen production. By using a water-splitting system with an aqueous electrolyte, this system is expected to block fire risks and enable stable hydrogen production.
KAIST (represented by President Kwang Hyung Lee) announced on the 22nd of October that a research team led by Professor Jeung Ku Kang from the Department of Materials Science and Engineering developed a self-powered hydrogen production system based on a high-performance zinc-air battery*.
*Zinc-air battery: A primary battery that absorbs oxygen from the air and uses it as an oxidant. Its advantage is long life, but its low electromotive force is a disadvantage.
Hydrogen (H₂) is a key raw material for synthesizing high-value-added substances, and it is gaining attention as a clean fuel with an energy density (142 MJ/kg) more than three times higher than traditional fossil fuels (gasoline, diesel, etc.). However, most current hydrogen production methods impose environmental burden as they emit carbon dioxide (CO₂).
While green hydrogen can be produced by splitting water using renewable energy sources such as solar cells and wind power, these sources are subject to irregular power generation due to weather and temperature fluctuations, leading to low water-splitting efficiency.
To overcome this, air batteries that can emit sufficient voltage (greater than 1.23V) for water splitting have been gaining attention. However, achieving sufficient capacity requires expensive precious metal catalysts and the performance of the catalyst materials becomes significantly degraded during prolonged charge and discharge cycles. Thus, it is essential to develop catalysts that are effective for the water-splitting reactions (oxygen and hydrogen evolution) and materials that can stabilize the repeated charge and discharge reactions (oxygen reduction and evolution) in zinc-air battery electrodes.
In response, Professor Kang's research team proposed a method to synthesize a non-precious metal catalyst material (G-SHELL) that is effective for three different catalytic reactions (oxygen evolution, hydrogen evolution, and oxygen reduction) by growing nano-sized, metal-organic frameworks on graphene oxide.
The team incorporated the developed catalyst material into the air cathode of a zinc-air battery, confirming that it achieved approximately five times higher energy density (797Wh/kg), high power characteristics (275.8mW/cm²), and long-term stability even under repeated charge and discharge conditions compared to conventional batteries.
Additionally, the zinc-air battery, which operates using an aqueous electrolyte, is safe from fire risks. It is expected that this system can be applied as a next-generation energy storage device when linked with water electrolysis systems, offering an environmentally friendly method for hydrogen production.
< Figure 1. Illustrations of a trifunctional graphene-sandwiched heterojunction-embedded layered lattice (G-SHELL) structure. Schematic representation of a) synthesis procedures of G-SHELL from a zeolitic imidazole framework, b) hollow core-layered shell structure with trifunctional sites for oxygen reduction evolution (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER), and c) heterojunctions, eterojunction-induced internal electric fields, and the corresponding band structure. >
Professor Kang explained, "By developing a catalyst material with high activity and durability for three different electrochemical catalytic reactions at low temperatures using simple methods, the self-powered hydrogen production system we implemented based on zinc-air batteries presents a new breakthrough to overcome the current limitations of green hydrogen production."
<Figure 2. Electrochemical performance of a ZAB-driven water-splitting cell with G-SHELL. Diagram of a self-driven water-splitting cell integrated by combining a ZAB with an alkaline water electrolyzer.>
PhD candidate Dong Won Kim and Jihoon Kim, a master's student in the Department of Materials Science and Engineering at KAIST, were co-first authors of this research, which was published in the international journal Advanced Science on September 17th in the multidisciplinary field of materials science. (Paper Title: “Trifunctional Graphene-Sandwiched Heterojunction-Embedded Layered Lattice Electrocatalyst for High Performance in Zn-Air Battery-Driven Water Splitting”)
This research was supported by the Nano and Material Technology Development Program of the Ministry of Science and ICT and the National Research Foundation of Korea’s Future Technology Research Laboratory.
2024.10.22 View 520 -
KAIST Industrial Design’s Professor Sangmin Bae’s team selected as Top 20 of James Dyson Award 2024
KAIST (President Kwang-Hyung Lee) announced that the 'Oxynizer', a non-electrical medical oxygen generator for developing countries designed by Professor Sangmin Bae's team in the Department of Industrial Design, has been selected to be the Top 20 of the James Dyson Award 2024. At the same time, it was announced on the 16th that it was selected as one of the top 100 ‘Prototypes for Humanity’ 2024 and will be exhibited in Dubai in November.
< Photo 1. Photo of the award-winning team of Professor Sangmin Bae’s students of KAIST Department of Industrial Designs at the James Dyson Award 2024 announcement of the National Winners >
The James Dyson Award is a design award hosted by Sir James Dyson, founder of Dyson, and receives ideas for solving everyday problems from next-generation engineers and designers around the world, and selects and awards innovative and excellent designs every year.
The ‘Oxynizer’ developed by Professor Sangmin Bae’s team was selected as the winner of the screening within Korea in September after competing with 122 domestic teams, and was awarded a prize of 5,000 pounds for idea advancement, product development, and commercialization.
< Photo 2. A photo of Professor Sangmin Bae’s students’ award-winning achievement, ‘Oxynizer’ >
In addition, on October 16th, it was selected as one of the top 20 international winners among 1,911 competing works from 29 countries around the world. The international winner will be selected by Sir James Dyson and announced on November 13th. The international competition winner will receive a prize of £5,000, and the winner will receive an additional £30,000, giving them the opportunity to commercialize their idea.
‘Prototype for Humanity’ is a global project hosted by Art Dubai Group and carried out in collaboration with Dubai Future Foundation, Dubai Arts & Culture Authority, and Dubai International Financial Center. It is a forum for international cooperation where leading universities around the world, including Harvard University and MIT, participate to discuss global problems and solutions.
‘Oxynizer’ was selected on September 11 as one of the top 100 out of 3,000 entries submitted by universities in over 100 countries, and will be exhibited at the Jumeirah Emirates Towers of Dubai Future Foundation from November 17 to 22. The organizers will select the top five during the exhibition period, and will award a total of $100,000 in prize money to the winners to support their research.
The ‘Oxynizer’ is a device developed by students Jiwon Kim, Kyeongho Park, Seung-Jun Lee, Jiwon Lee, Yeohyeon Jeong, and Jungwoo Kim under the guidance of Professor Sangmin Bae of KAIST, and is the result of research conducted in the ‘Design Project 1’ class for the graduate students of the Department of Industrial Design at KAIST.
< Photo 3. A photo of Professor Sangmin Bae’s students’ award-winning achievement, ‘Oxynizer’ >
This device was designed to solve the problem of difficulty in supplying oxygen in developing countries due to high installation and maintenance costs. The device was designed to create concentrated oxygen to supply it to a patient in urgent need using an air pump for bicycles, which should be found more easily than a medical oxygen tank.
Professor Sangmin Bae said, “This device creates oxygen using a bicycle air pump and supplies it to patients, and it can separate water vapor and nitrogen in the air using silica gel and zeolite, which are the main materials of the filter, to supply oxygen with a concentration of up to 50%.” “In addition, the filter can be heated and reused after 120 hours of use, so it has the advantage of being able to be used semi-permanently,” he emphasized.
< Photo 4. A photo of Professor Sangmin Bae’s students’ award-winning achievement, ‘Oxynizer’ >
The results of the self-research derived from the KAIST Industrial Design Department class were selected as a world-class award winner and exhibition piece in competition with excellent universities around the world, once again proving the global competitiveness of the KAIST Industrial Design Department.
2024.10.16 View 514 -
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 580 -
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 1502 -
Unraveling Mitochondrial DNA Mutations in Human Cells
Throughout our lifetime, cells accumulate DNA mutations, which contribute to genetic diversity, or “mosaicism”, among cells. These genomic mutations are pivotal for the aging process and the onset of various diseases, including cancer. Mitochondria, essential cellular organelles involved in energy metabolism and apoptosis, possess their own DNA, which are susceptible to mutations. However, studies on mtDNA mutations and mosaicism have been limited due to a variety of technical challenges.
Genomic scientists from KAIST have revealed the genetic mosaicism characterized by variations in mitochondrial DNA (mtDNA) across normal human cells. This study provides fundamental insights into understanding human aging and disease onset mechanisms.
The study, “Mitochondrial DNA mosaicism in normal human somatic cells,” was published in Nature Genetics on July 22. It was led by graduate student Jisong An under the supervision of Professor Young Seok Ju from the Graduate School of Medical Science and Engineering.
Researchers from Seoul National University College of Medicine, Yonsei University College of Medicine, Korea University College of Medicine, Washington University School of Medicine National Cancer Center, Seoul National University Hospital, Gangnam Severance Hospital and KAIST faculty startup company Inocras Inc. also participated in this study.
< Figure 1. a. Flow of experiment. b. Schematic diagram illustrating the origin and dynamics of mtDNA alterations across a lifetime. >
The study involved a bioinformatic analysis of whole-genome sequences from 2,096 single cells obtained from normal human colorectal epithelial tissue, fibroblasts, and blood collected from 31 individuals. The study highlights an average of three significant mtDNA differences between cells, with approximately 6% of these variations confirmed to be inherited as heteroplasmy from the mother.
Moreover, mutations significantly increased during tumorigenesis, with some mutations contributing to instability in mitochondrial RNA. Based on these findings, the study illustrates a computational model that comprehensively elucidates the evolution of mitochondria from embryonic development to aging and tumorigenesis.
This study systematically reveals the mechanisms behind mitochondrial DNA mosaicism in normal human cells, establishing a crucial foundation for understanding the impact of mtDNA on aging and disease onset.
Professor Ju remarked, “By systematically utilizing whole-genome big data, we can illuminate previously unknown phenomena in life sciences.” He emphasized the significance of the study, adding, “For the first time, we have established a method to systematically understand mitochondrial DNA changes occurring during human embryonic development, aging, and cancer development.”
This work was supported by the National Research Foundation of Korea and the Suh Kyungbae Foundation.
2024.07.24 View 1607 -
The 3rd Global Entrepreneurship Summer School (GESS 2024) Successfully Completed in Silicon Valley
The 2024 Global Entrepreneurship Summer School (2024 KAIST GESS), hosted by the Office of Global Initiatives under the KAIST International Office (Director Man-Sung Yim), was held for the third time. This program allows students to visit Silicon Valley, a global startup hub, to directly experience its famous startup ecosystem and develop their capabilities for global expansion. A total of 20 students were selected through applications, interviews, final presentations, mentoring, and peer evaluations. Additionally, 17 students from the KAIST Impact MBA course at the KAIST Business School also participated.
Before starting the Silicon Valley program, participants received mentoring on business model development and pitching advice from a senior entrepreneur at KAIST for about two months, beginning last May. Afterward, they developed business items for each team at KAIST’s main campus in Daejeon. For seven days, starting from June 23rd, workshops were held under the themes of global entrepreneurship, learning through failure, capital and network, and startup culture at KOTRA Silicon Valley Trade Center, JP Morgan, and Plug and Play Tech Center. This program's lecture series provided prospective entrepreneurs with the opportunity to systematically learn the mindset and gain the experience needed to start a global business.
The participants also visited local companies and gained experience in the field of global technology startups. Visits included Bear Robotics (CEO John Ha), Soundable Health (CEO Cathering Song), ImpriMed (CEO Sungwon Lim), Phantom AI (CEO Hyunggi Cho), B Garage (CEO Aiden Kim), and Simple Steps (CEO Doyeon Kim). Lectures contained vivid experiences from Silicon Valley CEOs and company tours boosted the students' passion for entrepreneurship. In particular, Doyeon Kim, CEO of Simple Steps, which helps prevent career breaks for Korean female immigrants in Silicon Valley and allows talented female immigrants to demonstrate their abilities in society, said, “As a KAIST alumna entrepreneur, it was meaningful to share my experience with this generation of students who dream of starting a global business and creating social enterprises in the United States.”
This program also included a tour of Silicon Valley's big tech companies that have made a significant impact on the digital ecosystem through technological advancement and innovation. This included Broadcom, which maintains a strong global presence in the semiconductor and infrastructure software technology fields. At the invitation of Chairman Hock Tan, GESS participants had the opportunity to attend his lecture and ask questions. Chairman Tan, who received an honorary doctorate in engineering from KAIST last February, emphasized that experiencing failure and giving consistent effort over a long period of time are more important than anything else in order to grow as a global entrepreneur, and that technologies influencing the global market evolve over generations.
< Photo. Group photo of GESS 2024 participants at Broadcom with Chairman Hock Tan (center) ⓒBroadcom>
As part of this program, participants conducted a volunteer program called 'Let's play with AI+ Tech' with the Sunnyvale community in Silicon Valley and Foothill College to help grow together with the community. Through this program, GESS participants cultivated the virtues of a global leader. In this volunteer activity, low-income elementary school students and parents from the Sunnyvale community participated in chatbot training led by KAIST students, providing an opportunity to work with underprivileged groups in the local community.
In the final pitching event, the highlight of the program, local venture investors from Silicon Valley were invited as judges and evaluated the pitches for each team's business items. The participating students, who developed their own business models while receiving advice through face-to-face mentoring from a professional accelerator in Silicon Valley, showcased their creative and innovative ideas, presenting themselves as future global entrepreneurs. Merey Makhmutova (BS in Civil and Environmental Engineering) from the K-Bridge team, who won the final pitch, expressed her ambition: “Even before GESS pitch day, our team kept refining the pitch deck as we attended the lectures and benefitted from the mentoring. Our intense teamwork was a significant reason why we ultimately won first prize.” She added that K-Bridge aims to win an award at the upcoming UKC Pitching Competition and expressed her gratitude for being able to participate in this program. Arseniy Kan (BS in Electrical Engineering) from the KAIST Enablers team, who took second place, said, “The 2024 KAIST GESS Program became the most unforgettable and precious opportunity of my lifetime, and I dream of using this opportunity as a stepping stone to becoming a global entrepreneur.“ Additionally, Kangster (CEO Kang Kim), who won the Impact MBA final pitching session, had the opportunity to secure a meeting with a local investment company after their GESS final pitch.
The 2024 KAIST GESS was held in cooperation with the KAIST International Office, the KAIST College of Business, and Startup KAIST. Director Man-Sung Yim from the Office of Global Initiatives, who hosted the event, said, “KAIST students will grow into leaders with global influence and contribute to the international community by creating global value. At the same time, we hope to raise the international status of our university.” Professor Sangchan Park, who led the 17 Impact MBA students in this educational program, added, “Meeting with companies leading the global market and visiting Silicon Valley has been a valuable learning experience for students aiming to start a global startup.”
KAIST plans to continue promoting its global entrepreneurship education program by enriching its curriculum each year and helping students grow into entrepreneurs with the virtues of global leaders.
2024.07.03 View 2754 -
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 2258 -
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 7785 -
KAIST begins full-scale cooperation with Taiwan’s Formosa Group
< (From left) Senior Vice President for Planning and Budget Kyung-Soo Kim, and Professor Minee Choi of the Department of Brain and Cognitive Sciences of KAIST along with Chairman of Formosa Group Sandy Wang and KAIST President Kwang-Hyung Lee, and Dean Daesoo Kim of KAIST College of Life Science and Bioengineering >
KAIST is pursuing cooperation in the fields of advanced biotechnology and eco-friendly energy with Formosa Plastics Group, one of Taiwan's three largest companies.
To this end, Chairman Sandy Wang, a member of Formosa Group's standing committee and leader of the group's bio and eco-friendly energy sector, will visit KAIST on the 13th of this month. This is the first time that the owner of Formosa Group has made an official visit to KAIST.
Cooperation between the two institutions began last March when our university signed a memorandum of understanding on comprehensive exchange and cooperation with Ming Chi University of Science and Technology (明志科技大學), Chang Gung University(長庚大學), and Chang Gung Memorial Hospital(長庚記念醫院), three of many institutions established and supported by Formosa Group.
Based on this, Chairman Sandy Wang, who visits our university to promote more exchanges and cooperation, talked about ‘the education of children and corporate social return and practice of his father, Chairman Yung-Ching Wang,’ through a special lecture for the school leadership as a part of the Monthly Lecture on KAIST’s Leadership Innovation Day.
She then visited KAIST's research and engineering facilities related to Taiwan's future industries, such as advanced biotechnology and eco-friendly energy, and discussed global industry-academic cooperation plans. In the future, the two organizations plan to appoint adjunct professors and promote practical global cooperation, including joint student guidance and research cooperation. We plan to pursue effective mid- to long-term cooperation, such as conducting battery application research with the KAIST Next-Generation ESS Research Center and opening a graduate program specialized in stem cell and gene editing technology in connection with Chang Gung University and Chang Gung Memorial Hospital. The newly established cooperative relationship will also promote Formosa Group's investment and cooperation with KAIST's outstanding venture companies related to bio and eco-friendly energy to lay the foundation for innovative industrial cooperation between Taiwan and Korea.
President Kwang-Hyung Lee said, “The Formosa Group has a global network, so we regard it to be a key partner that will position KAIST’s bio and engineering technology in the global stages.” He also said, “With Chairman Sandy Wang’s visit, Taiwan is emerging as a global economic powerhouse,” and added, “We expect to continue our close cooperative relationship with the company.”
Formosa Group is a company founded by the late Chairman Yung-Ching Wang, the father of Chairman Sandy Wang. As the world's No. 1 plastic PVC producer, it is leading the core industries of Taiwan's economy, including semiconductors, steel, heavy industry, bio, and batteries. Chairman Yung-Ching Wang was respected by the Taiwanese people by setting an example of returning his wealth to society under the belief that the companies and assets he built ‘belonged to the people.’ Chang Gung University, Chang Gung Memorial Hospital, and Ming Chi University of Technology, which are pursuing cooperation with our university, were also established as part of the social contribution promoted by Chairman Yung-Ching Wang and are receiving financial support from Formosa Group.
2024.05.09 View 1233 -
Dr. Bong-Joon Jung Elected as Interim President of KAIST U.S. Alumni Association
On April 29th, 2024, the Office of Global Initiative at KAIST, led by Vice President Man-Sung Yim, successfully conducted the election for the interim president of the KAIST U.S. Alumni Association. The authority to organize and operate the election was delegated by the KAIST U.S. Alumni Interim President Selection Committee Foundation, consisting of Je Woo Kim, Director of KAIST U.S. Foundation; Terri Kim, Director of KAIST U.S. Foundation; and Myungjin Yim, President of the KAIST Silicon Valley Alumni Association.
The election was announced during the KAIST Online U.S. Alumni Event held on December 8th, 2023, with the aim of establishing interim executive principles for six months and ultimately forming the official executive body of the KAIST U.S. Alumni Association.
The election was conducted according to the requirements set by the KAIST U.S. Alumni Interim President Selection Committee Foundation, which stated that "if there is a single candidate, they will be elected if they receive more than half of the votes from participating voters." The voting period lasted from April 13th (Monday) to April 24th (Friday) and was specifically directed towards KAIST alumni living in the Americas.
All 50 eligible voters participated in the election, unanimously supporting Dr. Bong-Joon Jung (Ph.D., admitted in 1993, Department of Electrical Engineering) with a 100% approval rate as the interim president of the KAIST U.S. Alumni Association. Dr. Bong-Joon Jung currently serves as the Senior Technology Analyst and Partner at Ambex Venture Group and previously served as the President of the KAIST Silicon Valley Alumni Association. Dr. Jung expressed his intention to strengthen connections with KAIST alumni in the United States and contribute to the establishment of the U.S. alumni association as the interim president.
< Photo. Newly elected president of interim KAIST U.S. Alumni Association, Dr. Bong-Joon Jung. >
Man-Sung Yim, Vice President of the International Office, congratulated Dr. Bong-Joon Jung on his election and pledged continuous support for the activities of the interim KAIST U.S. Alumni Association, led by alumni. He stated, "The election of the interim president of the KAIST U.S. Alumni Association marks the beginning of active engagement not only from alumni associations in various regions but also from alumni across the Americas, reflecting sustained interest in their alma mater."
2024.04.29 View 1556