Research Interests

Calculation is increasingly replacing experimentation in design of useful functional materials. We use quantum mechanics and atomistic simulations for understanding and control of matter, energy, and information at the microscopic scales of materials science. When combined with machine learning, first-principles materials theory potentially leads to new opportunities for creativity in materials physics.
Materials by design

Materials by design

Condensed matter theory of real materials; Materials design for energy and information applications

Machine learning

Machine learning

New materials physics through machine learning; Materials theory for neuromorphic computing systems

Physics outside physics

Physics outside physics

Physics of complex systems; Order-disorder transitions in atomic clusters and proteins

 Work with us

Work with us

Our group uses computation to understand and predict materials properties from first principles (plus machine learning). All graduate students at DGIST are eligible for fully funded graduate scholarships. Admission information can be found here. We invite prospective students to get in touch with Prof. Kang at joongoo.kang@dgist.ac.kr. To learn more about our research, you may refer to Publication List.

Recent Publications

For more information, you may put your cursor on the figures. For the full publication list, please click on the link above in "Work with us".
Metal-free N2-to-NH3 thermal conversion at the boron-terminated zigzag edges of hexagonal boron nitride: Mechanism and kinetics
Metal-free N2-to-NH3 thermal conversion at the boron-terminated zigzag edges of hexagonal boron nitride: Mechanism and kinetics
Journal of Catalysis 375, 68 (2019)
Robust ferromagnetism in hydrogenated graphene mediated by spin-polarized pseudospin
Robust ferromagnetism in hydrogenated graphene mediated by spin-polarized pseudospin
Scientific Reports 8:13940 (2018)
A unified understanding of the direct coordination of NO to first-transition-row metal centers in metal–ligand complexes
A unified understanding of the direct coordination of NO to first-transition-row metal centers in metal–ligand complexes
Phys. Chem. Chem. Phys. 19, 28098 (2017)
Nonisovalent Si-III-V and Si-II-VI alloys: Covalent, ionic, and mixed phases
Nonisovalent Si-III-V and Si-II-VI alloys: Covalent, ionic, and mixed phases
Phys. Rev. B 96, 045203 (2017)
A unified understanding of the thickness-dependent bandgap transition in hexagonal two-dimessional semiconductorductors
A unified understanding of the thickness-dependent bandgap transition in hexagonal two-dimessional semiconductorductors
J. Phys. Chem. Lett. 7, 597 (2016)
Trilogy of STM-induced molecular anchoring on Au(111) (in collaboration with Prof. J. Seo)
Trilogy of STM-induced molecular anchoring on Au(111) (in collaboration with Prof. J. Seo)
J. Phys. Chem. C 119, 27721 (2015), Nanotechnology 27, 415711 (2016), and J. Phys. Chem. C 121, 17402 (2017)
Period-doubling reconstructions of semiconductor partial dislocations
Period-doubling reconstructions of semiconductor partial dislocations
NPG Asia Mater. 7, e216 (2015)
Tunable Anderson localization in hydrogenated graphene based on the electric field effect
Tunable Anderson localization in hydrogenated graphene based on the electric field effect
Phys. Rev. Lett. 111, 216801 (2013)

People

Computational Materials Theory