CMDS LAB

Computational Materials & Data-driven Science

School of Chemical Engineering Jeonbuk National University

Molecule on an atomic surface with a potential-energy landscape and data-driven modeling curves.

Understanding
reaction dynamics.
Discovering
novel materials.

Connecting physics-based simulations with AI to reveal mechanisms and guide materials discovery.

We connect molecular insight with materials discovery through computational chemistry and artificial intelligence. Our research explores how atomic interactions, interfacial environments, and structural dynamics govern reaction pathways and materials performance. By integrating first-principles simulations, multiscale kinetics, and data-driven modeling, we seek to reveal the mechanisms behind complex chemical processes and guide the design of catalysts, energy materials, functional materials, and molecular systems.

Our computational approach
First-principles simulationsMultiscale kineticsMachine learningHybrid modeling

From our research

Recent publications.

All publications
  1. 2026

    Separation of germanium from low-grade zinc mine tailings using membrane solvent extraction with Tributyl phosphate (TBP)

    Separation and Purification Technology

  2. 2026

    Direct visualization of complex mechanisms in lithium tellurium batteries

    Nature Communications

  3. 2026

    Dynamic Active Site Mechanism and Compositional Tuning in LaxSr1-xCoyFe1-yO3 (LSCF) Perovskites toward Efficient Oxygen Evolution

    EcoEnergy

  4. 2026

    Influence of composition, thickness, and 2D layer coupling on electrochemical CO2 reduction

    npj 2D Materials and Applications

Explore chemistry through computation.

Interested in computational chemistry, materials science, or AI? We welcome inquiries from undergraduate researchers and prospective M.S. and Ph.D. students.

Join our group