Research Interests

Computation is assuming a progressively crucial role in the design of functional materials with practical applications. Employing quantum mechanics and atomic simulations enables us to comprehend and manipulate matter, energy, and information at the fundamental scales of materials physics. Integration with machine learning further opens avenues for innovative approaches in materials science, offering potential breakthroughs in first-principles materials theory.
Materials by design

Materials by design

Quantum mechanical theory of functional materials for energy and information applications
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How can materials be tailored for targeted properties using QM?

Machine learning

Machine learning

New physics and chemistry through machine learning; Materials physics using machine-learned potentials
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How can we study structures and dynamics beyond human intuition?

Simple complexity

Simple complexity

Structures and dynamics of liquids and glasses; ML-driven statistical physics; Metadynamics for complex systems
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How does our complex universe arise out of simple physical laws?

Work with us

Work with us

Our research intersects materials physics, chemistry, and machine learning. We employ quantum and statistical mechanics, alongside machine learning, to understand and predict materials properties.

All graduate students at DGIST are eligible for fully funded graduate scholarships. Admission information can be found here (in English) or here (in Korean). We invite prospective students to get in touch with Prof. Kang at joongoo.kang@dgist.ac.kr.

Recent Publications

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Molecular surface doping of cellulose nanocrystals: A high-throughput computational study
Molecular surface doping of cellulose nanocrystals: A high-throughput computational study
Chem. Mater. 36, 11421 (2024)
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); J. Am. Chem. Soc. 146, 9293 (2024)
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)

People

Computational Materials Theory