Faculty
Full-time Faculty
JUN, KYUJUNGAssistant Professor
- Research AreasComputational materials science, machine-learning-driven materials modeling and design, electrochemi
- Telephone02-3290-5958
- Office정운오 IT 교양관 709호
- Homepagekyujungjun.github.io
- E-mailkyujung@korea.ac.kr
Education
Ph.D. (May 2024) University of California, Berkeley, Materials Science & Engineering
B.S. (Aug 2018) Seoul National University, Nuclear Engineering (Minor in Materials Science & Engineering)
B.S. (Aug 2018) Seoul National University, Nuclear Engineering (Minor in Materials Science & Engineering)
Research Field
• Atomistic Modeling of Energy Materials (Solids, Polymers, Liquids)
• Machine-Learning-Driven Molecular Simulations
• Mechanistic Understanding of Ion Transport
• Design of Electrochemical and Battery Materials
• AI-Driven Materials and Chemical Discovery
• Machine-Learning-Driven Molecular Simulations
• Mechanistic Understanding of Ion Transport
• Design of Electrochemical and Battery Materials
• AI-Driven Materials and Chemical Discovery
Career
Assistant Professor (2026.03–present)
Korea University, School of Mechanical Engineering (co-affiliated with School of Smart Mobility)
Postdoctoral Associate (2024.06–2026.02)
Massachusetts Institute of Technology, Department of Materials Science and Engineering
Korea University, School of Mechanical Engineering (co-affiliated with School of Smart Mobility)
Postdoctoral Associate (2024.06–2026.02)
Massachusetts Institute of Technology, Department of Materials Science and Engineering
Publications
K. Jun, G. Ceder et al., Lithium superionic conductors with corner-sharing frameworks, Nature Materials, 2022.
K. Jun, G. Ceder et al., Diffuion mechanisms of fast lithium-ion conductors, Nature Reviews Materials, 2024.
K. Jun, G. Ceder et al., The nonexistence of a paddlewheel effect in superionic conductors, PNAS, 2024.
B. Deng, P. Zhong, K. Jun, G. Ceder et al., CHGNet as a pretrained universal neural network potential for charge-informed atomistic modeling, Nature Machine Intelligence, 2023.
K. Jun, G. Ceder et al., Exploring the soft cradle effect and ionic transport mechanisms in the LiMXCl4 superionic conductor family, Matter, 2025.
K. Jun, R. Gomez-Bombarelli et al., Universal Framework for Decomposing Ionic Transport into Interpretable Mechanisms, arXiv.
P. A. Leon, K. Jun, R. Gomez-Bombarelli et al., Mechanistic Decomposition of Ion Transport in Amorphous Polymer Electrolytes via Molecular Dynamics, The Journal of Physical Chemistry Letters, 2025.
K. Jun, G. Ceder et al., Diffuion mechanisms of fast lithium-ion conductors, Nature Reviews Materials, 2024.
K. Jun, G. Ceder et al., The nonexistence of a paddlewheel effect in superionic conductors, PNAS, 2024.
B. Deng, P. Zhong, K. Jun, G. Ceder et al., CHGNet as a pretrained universal neural network potential for charge-informed atomistic modeling, Nature Machine Intelligence, 2023.
K. Jun, G. Ceder et al., Exploring the soft cradle effect and ionic transport mechanisms in the LiMXCl4 superionic conductor family, Matter, 2025.
K. Jun, R. Gomez-Bombarelli et al., Universal Framework for Decomposing Ionic Transport into Interpretable Mechanisms, arXiv.
P. A. Leon, K. Jun, R. Gomez-Bombarelli et al., Mechanistic Decomposition of Ion Transport in Amorphous Polymer Electrolytes via Molecular Dynamics, The Journal of Physical Chemistry Letters, 2025.
