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English(EN) Dynamic language model representations for multi-objective reaction optimisation

语言模型动态学习化学反应表示以进行优化

研究人员开发了一种新颖的方法,通过使用微调的语言模型从文本中动态学习表示来优化化学反应。该方法与高斯过程和贝叶斯优化相结合,可将表示适应于特定的反应系统,其性能优于传统的独热编码和分子描述符等方法。该技术成功应用于钯催化氰化反应和不对称氢化反应的优化,在预期实验中实现了高产率和高对映选择性。 AI

影响 这项研究展示了语言模型在科学发现中的新颖应用,有望加速化学合成和材料科学的发展。

排序理由 学术论文,描述了使用语言模型进行反应优化的新方法。[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 →

语言模型动态学习化学反应表示以进行优化

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学术论文,描述了使用语言模型进行反应优化的新方法。[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) · Joshua W. Sin, David Ming Segura, Bojana Rankovi\'c, Siu Lun Chau, Marius D. R. Lutz, Andrea Anelli, Ryan P. Burwood, Kurt P\"untener, Maximilian J. Notheis, Raphael Bigler, Philippe Schwaller ·

    多目标反应优化的动态语言模型表示

    arXiv:2609.11790v1 Announce Type: new Abstract: Optimising chemical reactions across multiple objectives, such as yield, selectivity, and safety, is central to chemical synthesis, and model-driven approaches depend critically on how reaction components are represented. Establishe…