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 physics. When combined with machine learning, first-principles materials theory potentially leads to new opportunities for creativity in materials science. As Frank Wilczek once said, "Our creative mastery over matter, through quantum theory, is still embryonic. The best is yet to come."
Materials by design

Materials by design

Condensed matter theory of real materials; Materials design for energy and information applications
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Will Density Functional Theory remain as the "Standard Model" of materials theory?
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Machine learning

Machine learning

New physics and chemistry through machine learning (ML); Materials physics of complex systems using ML-based interatomic potentials
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Can a machine "see" the Hilbert space as we see objects in the three-dimensional space?

Simple complexity

Simple complexity

Structures and dynamics of liquids and glasses; "Phase" transitions in intermediate systems, such as nanoclusters and small proteins
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How does our complex universe arise out of simple physical laws?
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 Work with us

Work with us

Our research is at the intersection of condensed-matter theory, chemistry, and machine learning. We use 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