most citedScalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2026

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…

physics.comp-ph2026

Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication

Xinran Wei, Yan Pan, Fusong Ju +8

Locality-driven integration is a pervasive computational pattern in quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or…

physics.chem-ph2026

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…

physics.chem-ph2026★ 1 cited

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,…

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…