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 (Nature Machine Intelligence, 2026)
- 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.† and Aron W.† Guiding generative models to uncover diverse and novel crystals via reinforcement learning. Nature Machine Intelligence (2026).2. 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).
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).