2 citations · 4 across the 4 of their papers we have counts for
4 papers
Hessian-Informed Conformation Augmentation for Potential Energy Surface Curvature in Machine Learning Interatomic Potentials
Bumju Kwak, Jeonghee Jo
While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applications, such as vibrational…
Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks
Bumju Kwak, Jeonghee Jo
Recent SO(3)-equivariant models embedded a molecule as a set of single atoms fixed in the three-dimensional space, which is analogous to a ball-and-stick view. This perspective pro…
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
Jeonghee Jo, Bumju Kwak, Byunghan Lee +1
A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent…
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Bumju Kwak, Jiwon Park, Taewon Kang +3
In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric…