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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.
Switching from Adam to SOAP or SOAP-Muon can dramatically accelerate training and boost accuracy in machine learning interatomic potentials.
