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English(EN) Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors

新的ANCHOR方法增强了晶体结构发现

研究人员开发了一种名为ANCHOR的新方法,用于从头晶体生成,将成分搜索与结构预测分开。ANCHOR利用一个GRPO成分策略,该策略使用多目标奖励和冻结的晶体结构预测(CSP)模型进行训练。与现有的从头生成模型相比,该方法显著提高了新颖且稳定的晶体结构的发现率,这通过MSUN和SUN指标的增加以及在MatterGen评估管道下的性能提升得到证明。 AI

影响 这种新方法可以通过提高晶体结构预测和生成的效率来加速新材料的发现。

排序理由 这是一篇详细介绍晶体生成新方法的学术论文。[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 →

新的ANCHOR方法增强了晶体结构发现

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这是一篇详细介绍晶体生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Emma Lei Hovmand, Jonas Elsborg, Melih Kandemir, Arghya Bhowmik ·

    让 CSP 成为你的 ANCHOR:基于冻结结构先验的自适应晶体搜索

    arXiv:2609.33407v2 Announce Type: replace-cross Abstract: De novo crystal generation (DNG) models decide where to search in composition space and how to generate structures with one set of weights. We argue that discovery is better served by separating the two. A crystal structur…