activity
20232026
most citedLong-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation

6 citations · 6 across the 6 of their papers we have counts for

collaborators

7 papers

physics.chem-ph2026

Reinforcement learning amortizes transition-state physics into one-step flow models

Yunyang Li, Zechang Sun, Kuang Yu +3

Transition-state searches remain a major bottleneck in reaction discovery, as identifying valid saddle-point structures requires numerous expensive quantum-chemical calculations. G…

cs.LG2026

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.…

cs.LG2026

E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory

Lin Huang, Chengxiang Huang, Ziang Wang +7

Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectures face critical scalability bottlene…

physics.chem-ph2025

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…

cs.LG2025

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…

cs.LG2025

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…

physics.chem-ph2023★ 6 cited

Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation

Yunyang Li, Yusong Wang, Lin Huang +7

Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem…