About
My research goal is to accelerate materials discovery with AI. I am interested in materials generation, property prediction, machine learning interatomic potentials (MLIP), and DFT-based database construction.
I am also interested in building AI computational scientists that orchestrate these capabilities — autonomously design, simulate, evaluate, and collect data for materials.
Publications
Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling
Kiyoung Seong, Sungsoo Ahn, Sehui Han, Changyoung Park
Preprint 2026
Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers
Kiyoung Seong, Seonghyun Park, Seonghwan Kim, Woo Youn Kim, Sungsoo Ahn
ICLR 2025
On Scalable and Efficient Training of Diffusion Samplers
Minkyu Kim*, Kiyoung Seong*, Dongyeop Woo, Sungsoo Ahn, Minsu Kim(* equal contribution)
NeurIPS 2025
Learning Collective Variables from BioEmu with Time-Lagged Generation
Seonghyun Park, Kiyoung Seong, Soojung Yang, Rafael Gómez-Bombarelli, Sungsoo Ahn
ICLR 2026
Energy-Based Generator Matching: A Neural Sampler for General State Space
Dongyeop Woo, Minsu Kim, Minkyu Kim, Kiyoung Seong, Sungsoo Ahn
NeurIPS 2025
Experience
- 2025.03 — Present
- 2025.09 — 2026.02
- 2023.06 — 2025.02
Researcher
POSTECH, CSE
- 2017.03 — 2023.02
B.S. in Mathematics Education (Advanced Mathematics Track)
Korea University