151 citations · 161 across the 9 of their papers we have counts for
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physics.chem-ph2023★ 2 cited
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…
physics.chem-ph2023★ 3 cited
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…