10 citations · 12 across the 11 of their papers we have counts for
5 papers · 1 filter
UBio-MolFM: Enabling Biomolecular Dynamics at DFT Accuracy and Atoms with One Untuned Potential
Lin Huang, Frank Peng, JiaJun Cheng +8
Ion conduction, membrane permeation and metal recognition hinge on electronic structure, yet first-principles simulation reaches only hundreds of atoms. UBio-MolFM lifts that ceili…
UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems
Lin Huang, Arthur Jiang, XiaoLi Liu +8
All-atom molecular simulation serves as a quintessential ``computational microscope'' for understanding the machinery of life, yet it remains fundamentally limited by the trade-off…
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,…
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…