12 citations · 14 across the 9 of their papers we have counts for
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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…
Agentic Fusion of Large Atomic and Language Models to Accelerate Superconductor Discovery
Mingze Li, Yu Rong, Songyou Li +16
Artificial intelligence has accelerated materials discovery through high-throughput prediction and generation, yet the decision problem remains a formidable bottleneck. While curre…
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…
Universally Invariant Learning in Equivariant GNNs
Jiacheng Cen, Anyi Li, Ning Lin +5
Equivariant Graph Neural Networks (GNNs) have demonstrated significant success across various applications. To achieve completeness -- that is, the universal approximation property…
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…
Large Language-Geometry Model: When LLM meets Equivariance
Zongzhao Li, Jiacheng Cen, Bing Su +4
Accurately predicting 3D structures and dynamics of physical systems is crucial in scientific applications. Existing approaches that rely on geometric Graph Neural Networks (GNNs)…
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,…
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Jiaqi Han, Jiacheng Cen, Liming Wu +13
Geometric graphs are a special kind of graph with geometric features, which are vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physi…