Catalyst Intelligence
for Sustainable Chemical Engineering

AI-native chemical engineer designing next-generation catalysts to combat climate change.
Fusing machine learning, quantum mechanics, and multiscale simulation to reinvent how we discover materials.

Core Research Areas

"It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong." - Richard Feynman

Catalyst Discovery

Active learning accelerates materials screening.

Multiscale Simulation

Graph neural networks enable atomic to nanoscale modeling. [Chem. Eng. J. 2024]

Experimental Realization

Theory-guided synthesis validates computational predictions. [Angew. Chem. 2025]

Selected Publications

(17+ peer-reviewed papers)

Understanding oxygen transfer on ceria with Pt single atoms for surface reaction

Choi, Y.†, Choung, S.†, Han, J.†, Hwang, J., Jin, H., Kim, Y., Kim, J., Park, J. Y.*, Han, J. W.*, Lee, H.*

Nature Communications, 17, 298 (2026)

From Atomic Motif to Realistic Single Atom Catalysts through Machine Learning Interatomic Potentials

Choung, S.†, Kim, M.†, Moon, J.†, Han, J. W.*

ACS Energy Letters, 10, 6288-6296 (2025)

Highly Durable Rh Single Atom Catalyst Modulated by Surface Defects on Fe-Ce Oxide Solid Solution

Kim, G., Choung, S., Hwang, J., Choi, Y., Kim, S., Shin, D., Han, J. W.*, Lee, H.*

Angew. Chem. Int. Ed., 64, e202421218 (2025)

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