For scientists training ML models of molecular systems, switching from Adam to SOAP or SOAP-Muon optimizers can improve both training speed and final model accuracy, with bigger gains when you have less labeled data.
This paper compares advanced optimizers (SOAP, Muon, SOAP-Muon) against Adam for training machine learning interatomic potentials—AI models that simulate molecular behavior. The researchers find these newer optimizers converge faster and achieve better accuracy, especially when training data is limited, suggesting optimizer choice significantly impacts MLIP performance.