Research Interests

"Are there magnets as light as graphite?", "Are there materials that will support 100 hours of battery life for your cell phone?" Calculation is increasingly replacing experimentation in design of useful functional materials. When combined with machine learning , first-principles materials theory potentially leads to new opportunities for creativity. We use quantum mechanics and atomistic simulations for understanding and control of matter, energy, and information at the microscopic scales of materials science.
Materials by design

Materials by design

Materials design for energy conversion, storage and transport; Condensed matter theory of real materials

Machine learning

Machine learning

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

Physics outside physics

Physics outside physics

Materials physics of complex systems; Order-disorder transitions in atomic clusters and biomolecules such as 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".
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. (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)
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)
[Paper I] J. Phys. Chem. C 119, 27721 (2015), [Paper II] Nanotechnology 27, 415711 (2016), and [Paper III] J. Phys. Chem. C 121, 17402 (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)
Period-doubling reconstructions of semiconductor partial dislocations
Period-doubling reconstructions of semiconductor partial dislocations
NPG Asia Mater. 7, e216 (2015)

People

Computational Materials Theory