59 citations · 158 across the 14 of their papers we have counts for
10 papers · 1 filter
Spin-Weighted Spherical Harmonics Enable Complete and Scalable -Equivariant Networks
Chenxing Liang, Yuchao Lin, Andrii Kryvenko +5
-equivariant networks are promising for 3D atomistic system modeling, yet their scalability is limited by the complexity of the Clebsch-Gordan Tensor Produc…
Language Models for Controllable DNA Sequence Design
Xingyu Su, Xiner Li, Yuchao Lin +3
We consider controllable DNA sequence design, where sequences are generated by conditioning on specific biological properties. While language models (LMs) such as GPT and BERT have…
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…
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…
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency
Yuchao Lin, Jacob Helwig, Shurui Gui +1
We consider achieving equivariance in machine learning systems via frame averaging. Current frame averaging methods involve a costly sum over large frames or rely on sampling-based…
SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations
Xuan Zhang, Jacob Helwig, Yuchao Lin +4
We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dy…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Periodic Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Yi Liu, Yuchao Lin +1
We consider representation learning on periodic graphs encoding crystal materials. Different from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on…
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
Limei Wang, Yi Liu, Yuchao Lin +2
Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either u…