NequIP

SOAP and Muon optimizers outperform Adam for training machine learning interatomic potentials

A new arXiv preprint systematically compares matrix-structured optimizers—Muon, SOAP, and SOAP-Muon—against Adam for training machine learning interatomic potentials (MLIPs), specifically NequIP and Allegro models. SOAP and SOAP-Muon consistently outperform Adam in both convergence speed and final accuracy, with gains especially pronounced under partial force supervision. The paper argues that optimizer choice is an underexplored but impactful design axis for scientific ML models.