2026-Present
AI Research Scientist II (2026.02 - Present)
Lila Sciences
Hyunsoo Park
Materials.AI Researcher
I am a Full Stacker of "Computational Materials Design"
Bridging Materials ScienceComputational ChemistryDeep Learning
Journey
2023-2026
Postdoc (2023.09 - 2026.02)
Imperial College London
Generative AIMLFFAgentic AI
London, United Kingdom
Main Projects
- Chemeleon2: GRPO for generative modeling for materials (arXiv, 2025)
- DAC-SIM: A molecular simulation package integrating MLFFs in MOFs for DAC (Matter, 2025) - Featured in KAIST News
- Chemeleon: A text-guided denoising diffusion model for materials generation (Nature Communications, 2025)
Perspectives
- Closing the synthesis gap in computational materials design (Nature Synthesis, 2026)
- Reflections on Europe academia as a postdoc (Matter, 2025)
- Has generative artificial intelligence solved inverse materials design? (Matter, 2024)
Awards
- Won 1st place in 2025 KRICT ChemDX Hackathon (MatSX)
- Won 1st place in Global LLM Hackathon for Applications in Materials Science & Chemistry (>1,000 participants from 23 countries; Awards, Article)
Advisor: Aron Walsh
2020-2023
PhD (2020.03 - 2023.08)
Korea Advanced Institute of Science and Technology (KAIST)
MOFTransformerTransfer LearningMOFs
Daejeon, South Korea
Main Project
- MOFTransformer: A multi-modal transformer for universal transfer learning in MOFs (Nature Machine Intelligence, 2023) - Featured in Nature Computational Science and C&EN
Advisor: Jihan Kim
2022
Visiting Researcher (2022.05 - 2022.10)
EPFL
Reinforcement LearningInverse DesignMOFs
Sion, Switzerland
Main Project
- MOFReinforce: RL framework for inverse design of MOFs for CO2 DAC application (Digital Discovery, 2024) - Featured in Digital Discovery Highly Cited Authors 2024
Advisor: Berend Smit
Featured
Tutorial
🔬
Materials Discovery Tutorial
Google ColabCrystal discovery pipeline: SMACT, Chemeleon-DNG, MACE, Stability Analysis
📖
Chemeleon2 Documentation
DocumentationVAE, LDM, and RL modules for crystal structure generation with GRPO
📓
MLFF Tutorial
Jupyter NotebookMACE fine-tuning for Metal-Organic Frameworks
Blog
Paper
Selected Papers
1. Hyunsoo P., Kinga O M., Panyalak D., Ryan N., Aron W. Closing the synthesis gap in computational materials design. Nature Synthesis. 5, 651-659 (2026).2. Hyunsoo P.†, Aron W.† Guiding Generative Models to Uncover Diverse and Novel Crystals via Reinforcement Learning. arXiv preprint (2025).
3. Yunsung L.*, Hyunsoo P.*, Aron W. and Jihan K. Accelerating CO₂ direct air capture screening for metal-organic frameworks with a transferable machine learning force field. Matter. 8, 102203 (2025).
4. Hyunsoo P.†, Anthony O. and Aron W.† Exploration of crystal chemical space using text-guided generative artificial intelligence. Nature Communications. 16, 4379 (2025).
5. Yeonghun K.*, Hyunsoo P.*, Berend S., and Jihan K. A Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-Organic Frameworks. Nature Machine Intelligence. 5, 309-318 (2023).
6. Hyunsoo P., Zhenzhu L. and Aron W. Has generative artificial intelligence solved inverse materials design?. Matter. 7, 2355-2367 (2024).