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English(EN) Predicting Space Groups of Double Perovskites by LLM with Dynamic Few-Shot Learning

LLM代理框架DyRIS改进了非平衡钙钛矿数据的空间群预测

研究人员开发了DyRIS,一个新颖的LLM代理框架,用于预测双钙钛矿(一类具有重要催化潜力的材料)的空间群。该任务的现有数据集通常是非平衡的,偏向于常见的空间群而非稀有的空间群。DyRIS通过采用动态的、增强多样性的少样本提示来检索相关示例,并结合基于晶体学领域知识的规则引导推理来优化预测,从而解决了这一问题。这种方法提高了准确性,特别是对于代表性不足的空间群,其性能优于现有的基于成分和基于描述符的方法。 AI

影响 增强了LLM在科学研究中的能力,特别是在材料科学中处理非平衡数据集方面。

排序理由 详细介绍使用LLM进行材料科学新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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LLM代理框架DyRIS改进了非平衡钙钛矿数据的空间群预测

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详细介绍使用LLM进行材料科学新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jongwon Park, Inhyo Lee, Junhyeong Lee, Seunghwa Ryu ·

    利用LLM结合动态少样本学习预测双钙钛矿空间群

    arXiv:2608.10483v1 Announce Type: new Abstract: Double perovskites (DPs) offer broad compositional tunability, but predicting the space groups (SGs) of stable structures remains difficult because available datasets are often strongly imbalanced toward dominant SG classes. We refe…