1 citations · 1 across the 6 of their papers we have counts for
6 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…
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…
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…
On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods
Montgomery Bohde, Meng Liu, Alexandra Saxton +1
Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in exist…