CMDD @ KAIST

Research theme

Atomistic modeling & AI

We use first-principles calculations and atomistic simulations to understand material stability, electronic structure, reaction mechanisms and ion transport. Machine-learned interatomic potentials extend simulations to larger systems and longer timescales, while machine learning supports materials discovery, property prediction and battery diagnostics. Predictions are tested against experiments to guide materials design.

The question

Predict properties.
Test the reasoning.

Which physical information and training data support predictions that transfer to new materials?

Our approach

Combine first-principles calculations, atomistic simulation, machine learning and structured materials data.

Connected research

Battery-material problems provide applications and tests for new methods.

People and facilities

Find subgroup descriptions, team assignments and project records, together with the lab’s synthesis, cell fabrication, analysis, automation and computing equipment.

Meet the group

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Tools & resources

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