超越Adam：SOAP和Muon用于机器学习原子间势的更快、标签高效训练
Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
AI总结：
本文实现并系统比较了Muon、SOAP及混合SOAP-Muon等矩阵结构化优化器，用于训练NequIP和Allegro MLIP模型，发现这些优化器在收敛速度和最终精度上显著优于Adam，尤其在部分力监督下改进更为明显。
摘要：
机器学习原子间势（MLIP）已成为AI科学模拟的标志。尽管在新架构和数据集上的努力导致了越来越准确和通用的模型，但训练优化器的选择在很大程度上仍未探索，社区默认使用Adam及其变体。在这里，我们实现并系统比较了一类最近提出的矩阵结构化优化器，包括Muon、SOAP和混合SOAP-Muon，用于训练NequIP和Allegro MLIP模型。我们发现这些优化器在收敛速度和最终精度上都能显著优于Adam。SOAP和SOAP-Muon成为稳健且持续强劲的方法，而Muon相对于Adam仅提供部分增益。在部分力监督下，改进尤为明显。我们的结果表明，优化器选择是MLIP中被忽视但具有影响力的设计轴。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
