Seonghwan Kim
AI-CRED Research Fellow, KAIST
AI-CRED Research Fellow, KAIST
Hi! I am an AI-CRED Research Fellow at KAIST, working with Prof. Woo Youn Kim and Prof. Sungsoo Ahn. I received my Ph.D. in Chemistry from KAIST, where I worked in the Intelligent Chemistry Lab under the supervision of Prof. Woo Youn Kim.
My research focuses on developing machine learning methods grounded in physical and chemical principles to accelerate atomistic simulations. I have built generative models for molecular structures using diffusion processes, flow matching, and Schrödinger bridges—with a particular emphasis on navigating atomistic energy landscapes, including transition state prediction and molecular structure optimization with quantum-chemical accuracy.
Building on this foundation, I am expanding toward simulation-driven approaches for drug discovery, including molecular dynamics acceleration, free energy calculations, and enhanced sampling of rare events. My goal is to make previously intractable atomistic computations feasible by combining rigorous mathematical frameworks with scalable GPU-based infrastructure.
Korea Advanced Institute of Science and Technology (KAIST) Sep. 2019 – Aug. 2025
Ph.D. in Chemistry Advisor: Prof. Woo Youn Kim
Korea Advanced Institute of Science and Technology (KAIST) Mar. 2013 – Feb. 2019
B.S. in Mathematics and Chemical & Biomolecular Engineering (Double Major)
Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
Seonghwan Kim*, Jeheon Woo*, Jun Hyeong Kim, Woo Youn Kim†
Nat. Comp. Sci. 6, 134–144, 2026
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
Seonghwan Kim*, Joongwon Lee*, Seokhyun Moon*, Hyunwoo Kim, Woo Youn Kim†
ICLR 2026
Diffusion-based generative AI for exploring transition states from 2D molecular graphs
Seonghwan Kim*, Jeheon Woo*, Woo Youn Kim†
Nat. Commun. 15, 341, 2024
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
Jun Hyeong Kim*, Seonghwan Kim*, Seokhyun Moon*, Hyeongwoo Kim*, Jeheon Woo*, Woo Youn Kim†
ICLR 2025
Collective Variable Free Transition Path Sampling with Generative Flow Network
Kiyoung Seong*, Seonghyun Park*, Seonghwan Kim, Woo Youn Kim, Sungsoo Ahn†
ICLR 2025
Dynamic Precision Approach for Accelerating Large-Scale Eigenvalue Solvers in Electronic Structure Calculations on Graphics Processing Units
Jeheon Woo*, Seonghwan Kim*, Woo Youn Kim†
J. Chem. Theory Comput. 2023, 19, 5, 1457–1465
MoAgent: A Hypothesis-Driven Multi-Agent Framework for Drug Mechanism of Action Discovery
Jun Hyeong Kim, Seokhyun Moon, Seonghwan Kim*, Junhyeok Jeon, Taein Kim, Jisu Seo, Songmi Kim, Woo Youn Kim†
NeurIPS 2025 AI4D3
Grand Prize, Samsung Electronics DS Division Industry-Academia Cooperation 2024
2nd Place, Samsung AI Challenge 2023
Recipient, 9th EDISON Computational Chemistry Software Competition 2019
Journal Reviewer Nat. Comput. Sci. (2025), PNAS (2024), Nat. Mach. Intell. (2024), IEEE (2025)
Conference Reviewer NeurIPS (2024, 2025), ICLR (2025, 2026)
