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Adaptive tensor product computation slashes MLIP runtime by $2.4\times$ and memory by $2.6\times$ without sacrificing accuracy, revealing computational depth's link to chemical complexity.
Equivariant MLIPs can achieve leading inference speeds while maintaining high accuracy, revolutionizing computational materials science workflows.
Switching from Adam to SOAP or SOAP-Muon can dramatically accelerate training and boost accuracy in machine learning interatomic potentials.
