1 citations · 1 across the 8 of their papers we have counts for
9 papers
Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
Xuan Zhang, Haiyang Yu, Chengdong Wang +3
We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT), which can be efficiently represented as linear combination coefficients of atomic orbit…
Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations
Yuchao Lin, Cong Fu, Zachary Krueger +6
-equivariant networks are the dominant models for machine learning interatomic potentials (MLIPs). The key operation of such networks is the Clebsch-Gordan (CG) tensor…
Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
Cong Fu, Yuchao Lin, Zachary Krueger +6
Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obt…
NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis
Mouadh Yagoubi, David Danan, Milad Leyli-Abadi +15
The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computa…
Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
Haiyang Yu, Yuchao Lin, Xuan Zhang +2
We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science.…
A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling
Jacob Helwig, Sai Sreeharsha Adavi, Xuan Zhang +11
We consider the problem of modeling high-speed flows using machine learning methods. While most prior studies focus on low-speed fluid flows in which uniform time-stepping is pract…
Learning to Discover Regulatory Elements for Gene Expression Prediction
Xingyu Su, Haiyang Yu, Degui Zhi +1
We consider the problem of predicting gene expressions from DNA sequences. A key challenge of this task is to find the regulatory elements that control gene expressions. Here, we i…
A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils
Jacob Helwig, Xuan Zhang, Haiyang Yu +1
Computational modeling of aerodynamics is a key problem in aerospace engineering, often involving flows interacting with solid objects such as airfoils. Deep surrogate models have…
Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction
Zhao Xu, Haiyang Yu, Montgomery Bohde +1
Recent advancements in equivariant deep models have shown promise in accurately predicting atomic potentials and force fields in molecular dynamics simulations. Using spherical har…