Developing metallic materials is crucial as they are utilized in a wide range of industries, including automotive, aerospace, energy, and infrastructure. Advancing metallic materials leads to improved performance, durability, and efficiency in industrial products. Innovations in metallic materials can also reduce environmental impact and energy consumption, playing a key role in sustainable development. Therefore, continuous research and development in this field are vital for technological progress and economic growth.

From this respect, our research lab is dedicated to designing advanced metallic materials with a focus on innovative solutions for various industries. By exploring new alloys and enhancing the properties of materials, we aim to address the evolving needs of the sectors, such as driving technological advances, improving processing efficiency, and contributing to sustainable development. To this end, we comprehensively analyze the crystal structure and microstructure using well-known facilities, perform thermodynamic and kinetic simulations, and obtain process-structure-property linkages through multiscale property measurements.

Selected Papers: 

(1) K. Jeong et al., Athermally enhanced recrystallization kinetics of ultra-low carbon steel via electric current treatment, Acta Materialia 232, 117925 (2022)

(2) S. Lee, K. Jeong et al., Development of functionally graded austenitic lightweight steel through electrically assisted pressure solid-state joining, Materials Science and Engineering: A 891, 146003 (2024)

(3)  H. M. Sung, K. Jeong et al., Effect of the Ni plating on Al–Cu dissimilar metal laser welded joint, Journal of Materials Research and Technology 31, 2473-2483 (2024)

(4) N. Kwak, K. Jeong et al., Bimodal structured chromium-tungsten composite as plasma-facing materials: Sinterability, mechanical properties, and deuterium retention assessment, Acta Materialia 262, 119453 (2024)

Our research lab specializes in multi-scale and multi-physics finite element simulations, a powerful computational approach that allows us to model and analyze complex material behaviors across different scales and physical phenomena. This technique is crucial for understanding how materials respond to various environments from microscopic and macroscopic perspective. By integrating multiple physical models—such as mechanical, thermal, and electrical properties—we can optimize material design, predict performance, and improve the reliability of components used in industries. Through this advanced simulation methodology, we are able to accelerate the development of cutting-edge materials and technologies while reducing experimentation time and costs.

Selected Papers: 

(1) K. Jeong et al., A fully coupled diffusional-mechanical finite element modeling for tin oxide-coated copper anode system in lithium-ion batteries, Computational Materials Science 172, 109343 (2020)

(2) H. Hyun, K. Jeong et al., Suppressing High‐Current‐Induced Phase Separation in Ni‐Rich Layered Oxides by Electrochemically Manipulating Dynamic Lithium Distribution, Advanced Materials 33, 2105337 (2021)

(3) S. G. Kang, K. Jeong et al., Athermal glass work at the nanoscale: engineered electron-beam-induced viscoplasticity for mechanical shaping of brittle amorphous silica, Acta Materialia 238, 118203 (2022)

(4) J. Wang, K. Jeong et al., A kinetic indicator of ultrafast nickel-rich layered oxide cathodes, ACS Energy Letters 8, 2986-2995 (2023)

Machine learning algorithms help identify hidden patterns in large datasets, enabling us to optimize material properties and reduce trial-and-error experimentation. Deep learning models further enhance our ability to simulate complex material behaviors and predict their performance under various conditions. This cutting-edge approach allows us to explore innovative alloys and tailor material properties, significantly advancing the field of materials science.

Our research lab focuses on the integration of machine learning and deep learning techniques with computational simulations to accelerate the development and characterization of materials. By leveraging these advanced technologies, we are able to efficiently analyze and predict the microstructure-property relationships of metals, which are crucial for designing high-performance materials.

Selected Papers: 

(1) K. Jeong et al., Prediction of uniaxial tensile flow using finite element-based indentation and optimized artificial neural networks, Materials & Design 196, 109104 (2020)

(2) K. Jeong et al., Deep learning-based indentation plastometry in anisotropic materials, International Journal of Plasticity 157, 103403 (2022)

(3) K. Jeong et al., Parameter determination of anisotropic yield function using neural network-based indentation plastometry, International Journal of Mechanical Sciences 263, 108776 (2024)

Our research lab emphasizes the importance of numerical analysis for semiconductor and battery systems, combining finite element analysis and deep learning techniques to model and optimize their performance. By using finite element simulation, we investigate the behavior of materials and components at a detailed level, considering factors like heat transfer, mechanical stress, and electrochemical reactions in semiconductors and batteries. This approach allows us to gain deeper insights into system performance, identify potential failures, and optimize designs before physical testing. Additionally, deep learning enhances our ability to predict complex, non-linear behaviors in these systems, further improving accuracy and efficiency. Our advanced numerical methods help accelerate the development of next-generation semiconductor technologies and energy storage solutions, contributing to innovations in electronics, electric vehicles, and renewable energy systems.