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cs.DC2026
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…
cs.DC2026
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…