no code implementations • 1 Jan 2024 • Shi Yin, Xinyang Pan, XUDONG ZHU, Tianyu Gao, Haochong Zhang, Feng Wu, Lixin He
Deep learning for Hamiltonian regression of quantum systems in material research necessitates satisfying the covariance laws, among which achieving SO(3)-equivariance without sacrificing the expressiveness capability of networks remains unsolved due to the restriction on non-linear mappings in assuring theoretical equivariance.
2 code implementations • 25 Jun 2019 • Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, Lixin He
In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.
Ranked #6 on Graph Regression on Lipophilicity