训练MLIP别再默认用Adam了，试试SOAP或SOAP-Muon，收敛更快精度更高，尤其在有部分力监督时提升明显。
论文系统比较了Muon、SOAP和SOAP-Muon三种优化器在训练NequIP和Allegro MLIP模型时的表现。实验发现SOAP和SOAP-Muon在收敛速度和最终精度上均显著超越Adam，其中SOAP-Muon综合最优。Muon仅提供部分改进，在部分力监督场景下这些优化器优势更加明显。结果表明优化器选择是MLIP训练中一个被忽视但影响巨大的设计维度。
Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
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.