Lab Vision

Atomistic simulations and AI for energy materials and catalysis.
We develop ML-driven simulation methods to understand how materials behave under real operating conditions.

Core Philosophy

A computational research group led by Dr. Seokhyun Choung, focused on energy materials and catalysis. Idealized slab models at 0 K are a good starting point, but real materials consist of defective nanoparticles, grain boundaries, and surfaces that restructure mid-reaction. We use machine learning interatomic potentials and large-scale molecular dynamics to simulate these systems as they actually are, and work closely with experimentalists to validate predictions.

AI for Catalyst Discovery Experiment Collaboration Atomistic Simulation
Research Directions

Operando Simulation & Experiment Collaboration

MLIP-driven molecular dynamics at realistic conditions: actual nanoparticle geometries, operating temperatures, reactive atmospheres, compared directly with in-situ experiments (XAS, DRIFT, TEM). 17+ experiment-theory co-publications.

Nat. Commun. (2025)
Appl. Catal. B (2025)
Appl. Catal. B (2026)

MLIP Methods for Targeted Systems

Adapting foundation models to specific material systems with minimal DFT data. Compressing large GNNs into fast, domain-accurate potentials (10-100x speedup), with generative modeling to explore configuration spaces.

FORK, AI4Mat-NeurIPS
ACS Energy Lett. (2025)
CatBench, Cell Rep. Phys. Sci.

AI-Augmented Discovery

AI agents handle the full discovery cycle: propose → simulate → analyze → suggest next experiment. LLMs, MLIP simulations, and analysis tools orchestrated into autonomous workflows.

CatAgent, ICLR 2026 AI4Mat
Review, Chem. Eng. J. (2024)
What We Offer
For Your Research
  • Claude Max for every lab member
  • GPU clusters (A6000/L40S, H100 via KISTI)
  • VASP, LAMMPS, PyTorch, ASE, custom tools
  • Direct experimental collaborator connections
For Your Life
  • Gym membership (we pay for it)
  • Flexible working hours (output > hours)
  • Unlimited deep discussions with Dr. Choung
  • 1-on-1 mentoring for YOUR career goals
  • Asking "why?" is encouraged, not punished
FAQ
Do I need prior DFT or ML experience?
No. We'll teach you everything. We care about curiosity and drive, not a perfect GPA or prior experience.
I only have experimental experience. Can I apply?
Yes, and we actually welcome it. Computational researchers who understand experiments run the best simulations. We'll teach the coding.
Is the lab English-friendly?
Yes. All group meetings and internal communication can be in English. Papers are in English. International students are welcome.
Do you really provide Claude Max?
Yes. AI tools are research infrastructure. We invest, not economize.
How many hours per week?
We don't count hours. Ask good questions, make steady progress, and take care of your health. That's it.
What's the lab culture like?
We value: intellectual honesty, curiosity, kindness, and taking care of yourself. We don't value: performative busyness, hierarchy for its own sake, or suffering as a badge of honor.
How are research topics decided?
Initially the advisor suggests directions, then you gradually take the lead. The ultimate goal is for you to find your own compelling questions.
Career paths after graduation?
Academia, national labs, industry R&D, AI/ML engineering, and more. The combination of atomistic simulation + AI skills is in demand everywhere.
How much coding skill do I need?
Basic Python is enough. You'll learn the rest on the job. AI tools have lowered the barrier significantly.

Interested?

Graduate students, postdocs, and undergrads all welcome. No simulation experience required.

No formal deadline. Positions open until filled.