10 citations · 12 across the 11 of their papers we have counts for
4 papers · 1 filter
Accurate and scalable exchange-correlation with deep learning
Giulia Luise, Chin-Wei Huang, Thijs Vogels +25
Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable…
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
Yunyang Li, Zaishuo Xia, Lin Huang +8
Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltoni…
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…
E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products
Yunyang Li, Lin Huang, Zhihao Ding +10
Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However…