Author

Weile Jia

3 papers indexed here

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Preprint Oct 2026

TrimSO(2): Fast and Memory-Efficient Training of Equivariant Interatomic Potentials

Equivariant interatomic potentials enable accurate and efficient simulations of molecules and materials. Among them, models based on SO(2) convolutions reduce the cost of tensor products and achieve state-of-the-art accuracy, but their training remains time-consuming and memory-intensive. Existing training implementati...

Chen Wang, Yuan-Chang Zhou, Guang-Ming Tan et al. · 0 citations

#machine learning Preprint Oct 2026

LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs

Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations. However, the high computational cost of their core Clebsch--Gordan tensor product (CGTP) convolutions severely limits model scalability and efficiency. In this pap...

Yuan-Chang Zhou, Ming-Zhen Li, Chen Wang et al. · 0 citations

Jul 2026

Libra: Taming Attention Workload Skew in Long-Context LLM Training with Bounded Sequence Pool

Libra is presented, which operationalizes the law of large numbers (LLN) as a scaling principle for load balancing: the attention-balancing pool need not grow with the DP degree.

Yan Wang, Xiu-Long Yuan, Kaiming Yang et al. · arXiv.org · 1 citation