English(EN) Accelerating SO(2)-Equivariant Materials Models through Low-Precision Emulation

FlashSO(2)框架通过低精度仿真加速材料建模

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了FlashSO(2)，一个旨在加速材料建模中SO(2)-等变图神经网络的训练和推理的新框架。该框架利用低精度仿真，特别是Ozaki整数方案，在不牺牲准确性的情况下实现显著的加速。与TF32等现有低精度格式相比，FlashSO(2)表现出更优越的性能，在NVIDIA GPU上提供更高的吞吐量和更小的内存占用。 AI

影响该框架有望显著减少训练和部署先进材料模型所需的计算资源，从而可能加速材料科学领域的发现。

排序理由该条目是一篇学术论文，详细介绍了一个用于加速材料科学中机器学习模型的新计算框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Denghui Lu, Alexander Maeder, Alexandros Nikolaos Ziogas, Mathieu Luisier, Manasa Kaniselvan · 2026-10-28 04:00

通过低精度仿真加速 SO(2)-等变材料模型

arXiv:2610.35164v1 Announce Type: new Abstract: Rotationally equivariant architectures improve the physical fidelity of machine-learned (ML) universal materials models based on graph neural networks (GNNs). However, their scalability is limited by the use of non-trivial, high-pre…

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通过低精度仿真加速 SO(2)-等变材料模型

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