Postdoc · Computational Materials Science · AI for Materials
I work at the intersection of materials science and machine learning, combining first-principles calculations, atomistic simulation and ML models to explore and understand materials.
🏛️ I'm currently a postdoc in the group of Gian-Marco Rignanese at UCLouvain, working on the ERC project GEMPROMISE.
- 🤖 Machine-learned interatomic potentials and graph neural networks for materials
- 🧱 Structure generation, analysis and materials databases
- ⚙️ Automated high-throughput simulation workflows
| Project | Description |
|---|---|
| asc | Atomic Structure Classification: a framework to develop and benchmark graph neural networks to classify atomistic structures, with an OVITO extension for real-time inference |
- Transformation-induced plasticity in CeO2-ZrO2 ceramics: Atomic-scale insights using a deep neural network potential
J.-Y. Zhang, G. Huynh, F.-Z. Dai, T. Albaret, D. Rodney — Acta Mater. (2025) DOI - Hidden Complexity in Stress‐Induced Phase Transformations of CeO2–ZrO2 Ceramics
G. Huynh, J.-Y. Zhang, T. Albaret, D. Rodney — J. Amer. Ceram. Soc. (2026) DOI - Structure and energetics of ceria-zirconia alloys: Insights from first principles in the ceria-poor region
G. Huynh, T. Albaret, D. Rodney — Phys. Rev. Materials (2025) DOI
➡️ Full list on Google Scholar
TorchSim · atomate2 · GRACE · MACE · pymatgen · ASE
|
|
|
|
- ✉️ Email: gael.huynh@uclouvain.be
- 🎓 Google Scholar: Gaël Huynh
- 🆔 ORCID: 0009-0003-1496-7685
- 💼 LinkedIn: gael-huynh