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Reducing Symmetry Increase in Equivariant Neural Networks
Ning Lin, Jiacheng Cen, Anyi Li +2
Equivariant Neural Networks (ENNs) have empowered numerous applications in scientific fields. Despite their remarkable capacity for representing geometric structures, ENNs suffer f…
Planar Symmetric Pattern Generation
Ning Lin, Luxi Chen, Huaguan Chen +4
Generating objects with specific symmetries is essential in various real-world scenarios. However, adapting existing 2D continuous representations to enforce planar group symmetry…
Geometric Mixture Models for Electrolyte Conductivity Prediction
Anyi Li, Jiacheng Cen, Songyou Li +3
Accurate prediction of ionic conductivity in electrolyte systems is crucial for advancing numerous scientific and technological applications. While significant progress has been ma…
Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning
Yuelin Zhang, Jiacheng Cen, Jiaqi Han +1
Equivariant Graph Neural Networks (GNNs) have achieved remarkable success across diverse scientific applications. However, existing approaches face critical efficiency challenges w…
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
Jiacheng Cen, Anyi Li, Ning Lin +3
Equivariant Graph Neural Networks (GNNs) that incorporate E(3) symmetry have achieved significant success in various scientific applications. As one of the most successful models,…
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
Rong Han, Wenbing Huang, Lingxiao Luo +5
Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for str…