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English(EN) NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

NEMORA: 用于长程原子学习的神经等变多极算子

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了NEMORA，这是一种新颖的神经等变快速多极方法扩展，专为长程原子学习而设计。该方法将解析多极展开和翻译算子推广到学习到的等变对应项，使其能够捕捉不同长度尺度上的多体相互作用，同时保持线性的时间和内存复杂度。NEMORA可以处理包含数十万个原子的系统，与短程模型相比，显著降低了力和能量误差，在准确性方面优于或媲美现有的长程扩展方法。 AI

影响 NEMORA对长程相互作用的高效处理有望加速更准确、更具可扩展性的原子模拟机器学习模型的开发。

排序理由 该集群包含一篇详细介绍一种新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

NEMORA: 用于长程原子学习的神经等变多极算子

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该集群包含一篇详细介绍一种新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jay L. Kaplan, Samuel Varner, Rebecca Willett, Juan J. de Pablo ·

    NEMORA：用于长程原子学习的神经等变多极算子

    arXiv:2610.10776v1 Announce Type: new Abstract: Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching quantum-chemical accuracy at a fraction of the computational cost. These models describe local atom…

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