AI RESEARCH
Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
arXiv CS.LG
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ArXi:2604.26143v1 Announce Type: cross First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning Interatomic Potentials (MLIPs) have drastically improved cost for a given accuracy, their inference cost remains a bottleneck for massive systems or long timescales. To address this, we