AI RESEARCH
Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
arXiv CS.LG
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ArXi:2604.15821v1 Announce Type: cross Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire periodic table, serve as foundational models for quantum-accurate physical simulations. However, uMLIP