Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials

Machine learning interatomic potentials (MLIPs) are increasingly used to simulate molecular and material behavior for scientific discovery, yet the optimizers used to train them have remained an overlooked design choice, with most researchers defaulting to Adam. This paper systematically benchmarks newer matrix-structured optimizers—Muon, SOAP, and a SOAP-Muon hybrid—on training NequIP and Allegro models. SOAP and its hybrid variant substantially outperform Adam in both speed and accuracy, especially when force supervision is limited. These findings could meaningfully accelerate materials science and chemistry simulations by making MLIP training faster and more label-efficient without needing new architectures. Authors: Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng, Laura Zichi, Chuin Wei Tan, Marc L. Descoteaux, Boris Kozinsky Paper: https://arxiv.org/abs/2607.02499v1