Research Interests

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

Materials by design

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

Machine learning

Machine learning

New physics and chemistry through machine learning; Materials physics of complex systems using machine-learned force fields
.
Can a machine "see" objects in the Hilbert space ?

Simple complexity

Simple complexity

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

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".
Identifying and clearing individual oxygen impurities on graphene through the use of NO2 as a radical scavenger
Identifying and clearing individual oxygen impurities on graphene through the use of NO2 as a radical scavenger
Carbon 215, 118490 (2023)
Enhanced reactivity of magic-sized inorganic clusters by engineering the surface ligand networks
Enhanced reactivity of magic-sized inorganic clusters by engineering the surface ligand networks
Chem. Mater. 35, 700 (2023)
Multiscale isomerization of magic-sized inorganic clusters chemically driven by atomic-bond exchanges
Multiscale isomerization of magic-sized inorganic clusters chemically driven by atomic-bond exchanges
Chem. Mater. 34, 9527 (2022)
Nonequilibrium charge-density-wave melting in 1T-TaS2 triggered by electronic excitation: A real-time time-dependent density functional theory study
Nonequilibrium charge-density-wave melting in 1T-TaS2 triggered by electronic excitation: A real-time time-dependent density functional theory study
J. Phys. Chem. Lett. 13, 5711 (2022)
Electric-field-tunable bandages in the inverse-designed nanoporous graphene/graphene heterobilayers
Electric-field-tunable bandages in the inverse-designed nanoporous graphene/graphene heterobilayers
Adv. Electron. Mater. 8, 2200252 (2022)
Work on metal-organic frameworks in collaboration with Prof. Jinhee Park's group
Work on metal-organic frameworks in collaboration with Prof. Jinhee Park's group
Chem 8, 1993 (2022); Science Advances 8, eade1383 (2022)
General gauge symmetry in the theory and simulation of heat transport in nonsolid materials
General gauge symmetry in the theory and simulation of heat transport in nonsolid materials
Phys. Rev. B 103, 174209 (2021)
Charge-induced magnetic instability of atomically thin ferromagnetic semiconductors: The case of CrI3
Charge-induced magnetic instability of atomically thin ferromagnetic semiconductors: The case of CrI3
Phys. Rev. B 104, 085414 (2021)

People

Computational Materials Theory