1 citations · 1 across the 3 of their papers we have counts for
5 papers
Elign: Equivariant Diffusion Model Alignment from Foundational Machine Learning Force Fields
Yunyang Li, Lin Huang, Luojia Xia +2
Generative models for 3D molecular conformations must respect Euclidean symmetries and concentrate probability mass on thermodynamically favorable, mechanically stable structures.…
Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing
Chu Wang, Lin Huang, Xinran Wei +4
Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However,…
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…