Machine learning interatomic potentials (MLIPs) have become a hallmark of AI for scientific simulation. While efforts on new architectures and datasets have led to increasingly accurate and general models, the choice of optimizer for training has largely remained unexplored, defaulting to Adam and its variants in the community. Here, we implement and systematically compare a class of recently proposed matrix-structured optimizers, including Muon, SOAP, and the hybrid SOAP-Muon, for training NequIP and Allegro MLIP models. We find that these optimizers can substantially outperform Adam in both convergence speed and final accuracy. SOAP and SOAP-Muon emerge as robust and consistently strong methods, while Muon only provides partial gains relative to Adam. The improvements are particularly pronounced under partial force supervision. Our results indicate that optimizer choice is an overlooked yet impactful design axis for MLIPs.
超越Adam：SOAP与Muon优化器助力机器学习原子间势能模型的快速、省标签训练 / Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
该论文发现，在训练机器学习原子间势能模型时，使用SOAP和SOAP-Muon等新型矩阵结构优化器，比传统Adam优化器不仅能显著加快收敛速度、提升最终精度，还能在仅使用部分力标签的情况下表现更优，为提升材料模拟效率提供了重要新思路。
源自 arXiv: 2607.02499
