Research Areas

Computational Materials Science, Energy Materials, Machine Learning


Research Interests


  • Computational Materials Science (CMS) is one of R&D tools to understand the nature. CMS imitates the nature as a simple model and simulates the simplified model in the computer system. CMS 1) finds key factors for developing new materials of desired properties from the complex experimental effects using the simple model systems, 2) provides high-throughput virtual screening among the experimentally large number of candidate materials compared to the Edisonian approach (trial and error) of time- consuming and economically expensive experiment, 3) brings new insights into the development of new materials to guide experiments into a promising direction. Therefore, CMS can design new materials of desired properties through the key factors, high- throughput virtual screening and new insights.


  • Environmental issues have generated global attention for the development of eco-friendly renewable energy and storage technologies. On the next-generation secondary battery technology roadmap with higher energy density, safety, durability, faster charging and lower cost, all-solid-state batteries will continue the next generation mainstream and metal- air batteries will be the last step in the visible future. The use of CMS in this next- generation secondary battery roadmap will play a very important role for the development of original material technology. Especially, we are focusing on developing new catalysts for several electrochemical reactions,HER/OER/ORR/NRR/CO2RR etc, and new solid-state electrolyte materials.


  • Materials scientists are constantly striving to advance their ability to understand, predict, and improve materials properties. Material scientists have increasingly relied on simulation and modeling methods to understand and predict materials properties a priori. Materials informatics (MI) is a resulting branch of materials science that utilizes high-throughput computation to analyze large databases of materials properties to gain unique insights. More recently, data-driven methods such as machine learning (ML) have been adopted in MI to study the wealth of existing experimental and computational data in materials science, leading to a paradigm shift in the way materials science research is conducted. We are focusing on developing Moment Tensor Potential (MTP) as a Machine Learned Interatomic Potential (MLIP) and applying Active Learning to develop new materials.


Selected Publications

  1.  Directing the Surface Atomic Geometry on Copper Sulfide for Enhanced Electrochemical Nitrogen Reduction.
    ACS Catal., 2022, 12, 13638
  2. Designing Descriptor for the Computational Screening of Argyrodite-based Solid-State Superionic Conductors: Uniformity of Ion-Cage Size.
    J. Mater. Chem. A, 2022, 10, 7888
  3. Guanidinium-Pseudohalide Perovskite Interfaces Enable Surface Reconstruction of Colloidal Quantum Dots for Efficient and Stable Photovoltaics.
    ACS Nano, 2022, 16, 1649
  4. Bifunctional Covalent Organic Framework-Derived Electrocatalysts with Modulated p- Band Centers for Rechargeable Zn–Air Batteries.
    Adv. Func. Mater., 2021, 31, 2101727
  5. Ampere-hour scale flexible zinc-air pouch cells.
    Nature Energy, 2021, 6, 592
  6. Designing High-Performance Nitrogen-Doped Titanium Dioxide Anode Material for Lithium-Ion Batteries by Unravelling Nitrogen Doping Effect.
    Nano Energy, 2020, 74, 104829
  7. Molecular Engineering of Nanostructures and Activities on Bifunctional Oxygen Electrocatalysts for Zinc-Air Battery.
    Appl. Catal. B, 2020, 270, 118869
  8. Densely colonized isolated Cu-N single sites for efficient bifunctional electrocatalysts and rechargeable advanced Zn-air batteries.
    Appl. Catal. B, 2020, 268, 118746
  9. Unraveling the Controversy over a Catalytic Reaction Mechanism using a New Theoretical Methodology: One Probe and Non-Equilibrium Surface Green's Function. 
    Nano Energy, 2020, 63, 103863
  10. Unveiling dual-linkage 3D hexaiminobenzene metal-organic frameworks towards long- lasting advanced reversible Zn-air batteries.
    Energy Environ. Sci., 2019, 12, 727
  11. Hierarchically Designed 3D Holey C2N Aerogels as Bifunctional Oxygen Electrodes for Flexible and Rechargeable Zn-Air Batteries.
    ACS Nano, 2018, 12, 596 Most read article
  12. Scalable 3-D carbon nitride sponge as an efficient metal-free bifunctional oxygen electrocatalyst for rechargeable Zn-air batteries.
    ACS Nano, 2017, 11, 347


Professional Experience

  1. LG화학 기술연구원 부장 (2001.2 - 2012.8)
  2. 울산대학교 화학과 조교수 (2012.9 - 2014.8)
  3. 한양대학교 ERICA 화학분자공학과 교수 (2014.9 - 2023.2) 

Research Projects

  1. 삼성 SDI : OLED Colorant [2014]
  2. 삼성 SDI : LIB Cathode Materials [2015.03.01 ~ 2016.02.28]
  3. 삼성 SDI : LIB Cathode Materials [2015.03.01 ~ 2016.02.28]
  4. 신진연구사업: Nano-Electronics [2015.07.01 ~ 2018.06.30]
  5. 삼성미래기술육성재단 (I) : High mobility @ Organic Crystal [2014.06.01 ~ 2017.05.31]
  6. 삼성미래기술육성재단 (II) : High mobility @ Superlattice structure [2017.09.01 ~ 2020.08.31]
  7. 삼성미래기술육성재단(III) : LIB Cathode Materials [2016.06.01 ~ 2018.05.31]
  8. 미래소재디스커버리 (I) : Multi-level conducting [2015.12.04 ~ 2021.12.03]
  9. 중견연구사업: NEGF & Catalyst [2018.03.01 ~ 2021.02.28]
  10. L&F : NMC cathode [2018.09.01 ~ 2018.11.30]
  11. 현대자동차미래기술 (1): Solid State Electrolyte [2019.08.12 ~ 2020.02.11]
  12. 현대자동차 (1차년도): Solid State Electrolyte (Argyrodite) [2019.12.16 ~ 2020.12.15]
  13. 세라믹연구소: SOFC  [2021.03.09 ~ 2021.05.31]
  14. 현대자동차 (2차년도): Solid State Electrolyte (Sulfide & Halide) [2021.05.01 ~ 2022.04.30]
  15. 세라믹연구소: SOFC (Perovskite & Ceria) [2021.10.28 ~ 2022.02.13]
  16. GRRC : Catalyst [2020.09.01 ~ 2026.06.30]
  17. 현대자동차 (3차년도): Solid State Electrolyte (Sulfide & Halide) [2022.07.01 ~ 2023.06.30]
  18. 중견후속연구사업: Catalyst [2021.03.01 ~ 2024.02.29]
  19. 미래소재디스커버리 (II) : Solid State Electrolyte & Catalyst [2018.07.16 ~ 2024.07.15]
  20. 가상공학플랫폼구축사업: Solid State Electrolyte & Catalyst [2022.07.01 ~ 2026.12.31]