Seonghwan Kim

About Me

Hello! I am a graduate student in the Department of Chemistry at KAIST, working under the guidance of Professor Woo Youn Kim. My research focuses on computational chemistry and leveraging machine learning to explore chemical reactions. Specifically, I am interested in computer-aided elucidation of chemical reactions, with an emphasis on deep learning-based activity prediction and generative modeling of reaction configurations. Additionally, I am conducting research on functional molecular design using generative modeling techniques.

Publications

FragFM: Efficient Fragment-Based Molecular Generation via Discrete Flow Matching

Joongwon Lee*, Seonghwan Kim*, Woo Youn Kim†

ICLR GEM workshop, 2025

Paper

Discrete Diffusion Schr\" odinger Bridge Matching for Graph Transformation

Jun Hyeong Kim*, Seonghwan Kim*, Seokhyun Moon*, Hyeongwoo Kim*, Jeheon Woo*, Woo Youn Kim†

ICLR, 2025

Paper

Collective Variable Free Transition Path Sampling with Generative Flow Network

Kiyoung Seong*, Seonghyun Park*, Seonghwan Kim, Woo Youn Kim, Sungsoo Ahn†

ICLR SPIGM, 2025

Paper

Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy

Jeheon Woo*, Seonghwan Kim*, Woo Youn Kim†

arXiv preprint, 2024

Paper

Diffusion-based generative AI for exploring transition states from 2D molecular graphs

Seonghwan Kim*, Jeheon Woo*, Woo Youn Kim†

Nature Communications, 2024

Paper | Code

GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising

Hyeonsu Kim*, Jeheon Woo*, Seonghwan Kim*, Seokhyun Moon*, Junhyung Kim*, and Woo Youn Kim†

NeurIPS, 2023

Paper | Code

Gaussian-Approximated Poisson Preconditioner for Iterative Diagonalization in Real-Space Density Functional Theory

Jeheon Woo*, Seonghwan Kim*, Woo Youn Kim†

The Journal of Physical Chemistry A, 2023

Paper | Code

Dynamic Precision Approach for Accelerating Large-Scale Eigenvalue Solvers in Electronic Structure Calculations on Graphics Processing Units

Jeheon Woo*, Seonghwan Kim*, Woo Youn Kim†

Journal of Chemical Theory and Computation, 2023

Paper | Code

Awards

Recipient, 9th EDISON Computational Chemistry software application competition

Recipient, 2023 Samsung AI Challenge, 2nd Award

Recipient, Award for Industry-Academia Cooperation at the Final Exchange Meeting of Samsung Electronics DS Division (Grand Prize)