10 citations · 12 across the 11 of their papers we have counts for
11 papers
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…
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…
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
Yuxuan Ren, Dihan Zheng, Chang Liu +7
In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning mod…
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
Zun Wang, Chang Liu, Nianlong Zou +5
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Mode…