Connectivity optimized nested line graph networks for crystal structures
Graph neural networks (GNNs) have been applied to a large variety of applications in
materials science and chemistry. Here, we systematically investigate the graph construction …
materials science and chemistry. Here, we systematically investigate the graph construction …
Connectivity optimized nested graph networks for crystal structures
Graph neural networks (GNNs) have been applied to a large variety of applications in
materials science and chemistry. Here, we recapitulate the graph construction for crystalline …
materials science and chemistry. Here, we recapitulate the graph construction for crystalline …
Materials fatigue prediction using graph neural networks on microstructure representations
The local prediction of fatigue damage within polycrystals in a high-cycle fatigue setting is a
long-lasting and challenging task. It requires identifying grains tending to accumulate plastic …
long-lasting and challenging task. It requires identifying grains tending to accumulate plastic …
Edge Direction-invariant Graph Neural Networks for Molecular Dipole Moments Prediction
YJ Park - arXiv preprint arXiv:2206.12867, 2022 - arxiv.org
The dipole moment is a physical quantity indicating the polarity of a molecule and is
determined by reflecting the electrical properties of constituent atoms and the geometric …
determined by reflecting the electrical properties of constituent atoms and the geometric …