English(EN) Learning Nuclear Structure with AI: Radii and Collectivity

AI模型NuCLR推动核结构分析进展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了NuCLR（核协同学习表示），一个旨在分析核数据的AI模型。这个多任务模型从核素图谱的实验信息中学习，以预测电荷半径和电四极跃迁强度。NuCLR在性能上可与最先进的核模型相媲美，在电荷半径方面达到0.0147 fm的均方根偏差，在B(E2)强度方面达到0.192 e²b²。该模型还估计了不同核素的预测准确性，确定了新数据可以增强理解的领域。 AI

影响增强了核物理中理论外推和实验设计的驱动数据经验基线。

排序理由详细介绍用于科学研究的新AI模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Giuliano Giacalone, Sokratis Trifinopoulos, Mike Williams · 2026-09-17 04:00

AI学习核结构：半径与集体性

arXiv:2609.17838v1 Announce Type: cross Abstract: Low-energy nuclear structure is encoded in a broad body of experimental information across the chart of nuclides. Learning how this information is organized across observables and nuclei can provide a data-driven empirical baselin…

本文如何被排名

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AI学习核结构：半径与集体性

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