14 citations · 19 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 5 cited
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
Zeren Shui, Daniel S. Karls, Mingjian Wen +3
For decades, atomistic modeling has played a crucial role in predicting the behavior of materials in numerous fields ranging from nanotechnology to drug discovery. The most accurat…
cs.LG2020★ 14 cited
Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
Zeren Shui, George Karypis
As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecule…