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cs.LG2024
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields
Shihao Shao, Haoran Geng, Zun Wang +1
Machine Learning Force Fields (MLFFs) are of great importance for chemistry, physics, materials science, and many other related fields. The Clebsch-Gordan Transform (CG transform)…
cs.LG2024
SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
Hongfei Wu, Lijun Wu, Guoqing Liu +3
In this paper, we develop SE3Set, an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning. Hypergraphs are not merely an…
cs.LG2022★ 2 cited
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
Yusong Wang, Shaoning Li, Zun Wang +4
In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a gi…