Chunghee Nam

Hannam University — South Korea

Physics and Astronomy · Physical Sciences

13h-index597citations91works0.82yr avg
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Research Topics

Magnetic properties of thin films(48), Magnetic Properties and Applications(26), ZnO doping and properties(12), Magnetic and transport properties of perovskites and related materials(12), Metallic Glasses and Amorphous Alloys(10)

Publications91 total

Design of High-Strength High-Entropy Alloys Using a Variational Autoencoder-Based Inverse Design
Korean Journal of Materials Research·2025Open Access
Optimization of Machine Learning Models for Predicting the Saturation Magnetization of SmCo Permanent Magnet Materials
Korean Journal of Materials Research·2025Open Access
Điều khiển thích nghi bền vững cho một lớp phi tuyến affine bất định
Journal of Measurement Control and Automation·2025Open Access
Convolutional neural network-based prediction of hardness in bulk metallic glasses with small data
Journal of Non-Crystalline Solids·2025· 1 cited
Feature Selection and its Effects on Prediction of Saturation Magnetic Flux Density of Fe-based Bulk Metallic Glasses
Journal of the Korean Magnetics Society·2024
Prediction of supercooled liquid region of Fe-based metallic glasses by deep learning
Applied Physics A·2024· 2 cited
Optimization of a Genetic Algorithm-Based Machine Learning Model for Predicting the Magnetocaloric Effect of Bulk Metallic Glasses
Journal of the Korean Magnetics Society·2024
Prediction of Transition Temperature and Magnetocaloric Effects in Bulk Metallic Glasses with Ensemble Models
Korean Journal of Materials Research·2024Open Access
Deep learning-based prediction of saturation magnetic flux density in Fe-based metallic glasses via transfer learning
Materials Chemistry and Physics·2024· 4 cited
Magnetocaloric properties predicted by deep learning with compositional features for bulk metallic glasses
Journal of Non-Crystalline Solids·2023· 3 cited
Artificial Neural Network Supported Prediction of Magnetic Properties of Bulk Metallic Glasses
Korean Journal of Materials Research·2023Open Access
Compositional Feature Selection and Its Effects on Bandgap Prediction by Machine Learning
Korean Journal of Materials Research·2023· 1 citedOpen Access
Convolutional neural network guided prediction of saturation magnetic flux density of Fe-based metallic glasses
Computational Materials Science·2023· 10 cited
Prediction of mechanical properties of high-entropy ceramics by deep learning with compositional descriptors
Materials Today Communications·2023· 17 cited
Magnetocaloric Properties Predicted by Deep Learning with Compositional Features for Bulk Metallic Glasses
SSRN Electronic Journal·2023Open Access
Deep Learning-Based Prediction of Saturation Magnetic Flux Density in Fe-Based Metallic Glasses Via Transfer Learning
SSRN Electronic Journal·2023Open Access
Machine Learning Directed Prediction of Saturation Magnetization
Journal of the Korean Magnetics Society·2022
Super-Resolution Transmission Electron Microscope Image of Nanomaterials Using Deep Learning
Korean Journal of Materials Research·2022Open Access
Machine Learning Guided Prediction of Superhard Materials Based on Compositional Features
Korean Journal of Metals and Materials·2022· 5 citedOpen Access
Human Activity Recognition Using Machine Learning Based Mobile Smart Phone Magnetometers
Journal of the Korean Magnetics Society·2021
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Frequent Co-authors

Youngman Jang(10), C. A. Ross(10), M. D. Mascaro(7), Kisu Lee(6), B. K. Cho(5), B. G. Ng(4), Sung‐Myung Ryu(4), Seungha Yoon(4), Seonghoon Lee(3), Jungjin Shim(3), Jung Inn Sohn(2), Y. J. Cho(2), Kwang‐Seok Kim(2), Soo-Min Park(2), Myungsuk Song(2), Fernando Castaño(2), B K Cho(2), Ki-Su Lee(2), Youngsoo Jang(2), Won Bae Kim(2)