11 citations · 31 across the 14 of their papers we have counts for
6 papers · 1 filter
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Erpai Luo, Xinran Wei, Lin Huang +7
Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph n…
Neural PM: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Yusong Wang, Chaoran Cheng, Shaoning Li +5
Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range inter…
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
Zun Wang, Chang Liu, Nianlong Zou +5
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Mode…
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…
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
He Zhang, Chang Liu, Zun Wang +5
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its…
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
Siyuan Liu, Yusong Wang, Tong Wang +6
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential ro…