2 citations · 3 across the 9 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…
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…
MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
Yuanchang Zhou, Siyu Hu, Xiangyu Zhang +3
Foundation MLIPs demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials…