3 citations · 5 across the 4 of their papers we have counts for
4 papers
Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
Jeheon Woo, Seonghwan Kim, Jun Hyeong Kim +1
We introduce a framework for molecular structure optimization using denoising model on a physics-informed Riemannian manifold (R-DM). Unlike conventional approaches operating in Eu…
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
Jun Hyeong Kim, Seonghwan Kim, Seokhyun Moon +3
Transporting between arbitrary distributions is a fundamental goal in generative modeling. Recently proposed diffusion bridge models provide a potential solution, but they rely on…
Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs
Seonghwan Kim, Jeheon Woo, Woo Youn Kim
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have…
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising
Hyeonsu Kim, Jeheon Woo, Seonghwan Kim +3
As quantum chemical properties have a dependence on their geometries, graph neural networks (GNNs) using 3D geometric information have achieved high prediction accuracy in many tas…