132 citations · 232 across the 8 of their papers we have counts for
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TrimSO(2): Fast and Memory-Efficient Training of Equivariant Interatomic Potentials
Chen Wang, Yuanchang Zhou, Guangming Tan +2
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 pr…
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
Yuanchang Zhou, Mingzhen Li, Chen Wang +3
Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations. However, the high compu…
Libra: Taming Attention Workload Skew in Long-Context LLM Training with Bounded Sequence Pool
Yan Wang, Xiulong Yuan, Kaiming Yang +16
Long-context LLM training suffers from a load-balancing problem that sequence packing does not solve. Packing samples into fixed-token sequences balances memory and linear-cost ope…
JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
Hongyu Wang, Weijian Liu, Hongtao Xu +4
Discovering atom-level phenomena requires molecular dynamics (MD) simulations with ab initio accuracy. Machine learning interatomic potentials (MLIPs) enable stable, high-accuracy…
Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
Yuanchang Zhou, Hongyu Wang, Yiming Du +12
Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire perio…