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EE Prof. Jung-Yong Lee research team

developed a high-efficiency and high-stability organic-inorganic hybrid solar cell production technology

that maximizes near-infrared light capture

 <Photo. (From left) Professor Jung-Yong Lee, Ph.D. candidate Min-Ho Lee, and Master’s candidate Min Seok Kim of the School of Electrical Engineering>

Existing perovskite solar cells, which have the problem of not being able to utilize approximately 52% of total solar energy, have been developed by a Korean research team as an innovative technology that maximizes near-infrared light capture performance while greatly improving power conversion efficiency. This greatly increases the possibility of commercializing next-generation solar cells and is expected to contribute to important technological advancements in the global solar cell market.

The research team of Professor Jung-Yong Lee of the KAIST EE and Professor Woojae Kim of the Department of Chemistry at Yonsei University developed a high-efficiency and high-stability organic-inorganic hybrid solar cell production technology that maximizes near-infrared light capture beyond the existing visible light range.

The research team suggested and advanced a hybrid next-generation device structure with organic photo-semiconductors that complements perovskite materials limited to visible light absorption and expands the absorption range to near-infrared.

In addition, they revealed the electronic structure problem that mainly occurs in the structure and announced a high-performance solar cell device that dramatically solved this problem by introducing a dipole layer*. *Dipole layer: A thin material layer that controls the energy level within the device to facilitate charge transport and forms an interface potential difference to improve device performance.

Existing lead-based perovskite solar cells have a problem in that their absorption spectrum is limited to the visible light region with a wavelength of 850 nanometers (nm) or less, which prevents them from utilizing approximately 52% of the total solar energy.

To solve this problem, the research team designed a hybrid device that combined an organic bulk heterojunction (BHJ) with perovskite and implemented a solar cell that can absorb up to the near-infrared region.

In particular, by introducing a sub-nanometer dipole interface layer, they succeeded in alleviating the energy barrier between the perovskite and the organic bulk heterojunction (BHJ), suppressing charge accumulation, maximizing the contribution to the near-infrared, and improving the current density (JSC) to 4.9 mA/cm².

The key achievement of this study is that the power conversion efficiency (PCE) of the hybrid device has been significantly increased from 20.4% to 24.0%. In particular, this study achieved a high internal quantum efficiency (IQE) compared to previous studies, reaching 78% in the near-infrared region.

< Figure. The illustration of the mechanism of improving the electronic structure and charge transfer capability through Perovskite/organic hybrid device structure and dipole interfacial layers (DILs). The proposed dipole interfacial layer forms a strong interfacial dipole, effectively reducing the energy barrier between the perovskite and organic bulk heterojunction (BHJ), and suppressing hole accumulation. This technology improves near-infrared photon harvesting and charge transfer, and as a result, the power conversion efficiency of the solar cell increases to 24.0%. In addition, it achieves excellent stability by maintaining performance for 1,200 hours even in an extremely humid environment. >

In addition, this device showed high stability, showing excellent results of maintaining more than 80% of the initial efficiency in the maximum output tracking for more than 800 hours even under extreme humidity conditions.

Professor Jung-Yong Lee said, “Through this study, we have effectively solved the charge accumulation and energy band mismatch problems faced by existing perovskite/organic hybrid solar cells, and we will be able to significantly improve the power conversion efficiency while maximizing the near-infrared light capture performance, which will be a new breakthrough that can solve the mechanical-chemical stability problems of existing perovskites and overcome the optical limitations.”

This study, in which KAIST School of Electrical Engineering Ph.D. candidate Min-Ho Lee and Master’s candidate Min Seok Kim participated as co-first authors, was published in the September 30th online edition of the international academic journal Advanced Materials. (Paper title: Suppressing Hole Accumulation Through Sub-Nanometer Dipole Interfaces in Hybrid Perovskite/Organic Solar Cells for Boosting Near-Infrared Photon Harvesting).

This study was conducted with the support of the National Research Foundation of Korea.

EE Prof. Yong-Hoon Kim’s team succeeded in accelerating calculations

for electronic structure in quantum mechanics

for the first time in the world using a convolutional neural network (CNN) model

< (from left):  Prof. Yong-Hoon Kim, Ph.D. candidate  Ryong Gyu Lee>

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.

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.

Professor Hyun Myung Awarded 2024 Hanbit Grand Prize

  <EE Prof. Hyun Myung won the 2024 Hanbit Grand Prize ⓒDaejeon MBC>

Professor Hyun Myung in the School of Electrical Engineering won the 2024 Hanbit Grand Prize.

The Hanbit Grand Prize, co-hosted by Hanwha Group and Daejeon MBC, celebrates its 20th anniversary this year and discovers and awards individuals who have served and contributed to various fields in the local community.

The Hanbit Grand Prize selects one winner from each of five categories (science and technology, education/sports promotion, culture and arts, social service, and regional economic development) and awards each winner 10 million won and a plaque. This year, a special award (Oh Sang-wook, two-time Paris Olympics fencing champion, Daejeon) was added.

Professor Hyun Myung, the winner of the Science and Technology category, has contributed to the development of autonomous navigation and locomotion by researching these fields for 16 years, and was recognized for his contribution to winning the international autonomous quadruped robot competition by developing the new blind locomotion control technology called ‘DreamWaQ’.

The awards ceremony was held at the Daejeon MBC Open Hall on October 24 and was broadcast on Daejeon MBC TV on October 29.

<Prof. Hyun Myung was awarded the Hanbit Award, as reported by Daejeon MBC News ⓒDaejeon MBC>

Prof. Kyeongha Kwon and Sang-Gug Lee Team

had developed electrochemical impedance spectroscopy (EIS) technology

Photo (from left): Ph.D. candidate Young-Nam Lee, Prof. Sang-Gug Lee, Prof. Kyeongha Kwon>

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.

EE 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.