Scholarlink

<p>The development of porous crystalline materials with targeted properties remains challenging owing to the vast chemical design space and the high cost of experimental screening. Here we develop an artificial-intelligence-assisted interactive experiment–learning evolution approach to accelerate the discovery of highly fluorescent covalent organic frameworks (COFs). This approach integrates model recommendation, experimental validation and active learning in an iterative refinement cycle, allowing the artificial intelligence model to evolve along the process. Among the 520 possible combinations derived from a library of 20 amine and 26 aldehyde building blocks, we needed to experimentally evaluate only 11 COFs to identify one with a remarkable photoluminescence quantum yield of 41.3%. By embedding electronic configuration and quantum-level insights into the learning process, this approach transcends intuition based on statistical analysis intuition to enable material discovery driven by chemical knowledge, enhancing prediction reliability and interpretability. We also reveal the fluorescence mechanism of these COFs and outline the critical role of HOMO–LUMO alignment and excited-state charge distribution.</p><p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Discovery of highly fluorescent covalent organic frameworks through AI-assisted iterative experiment–learning cycles

  • 期刊论文

  • 发表日期：

    2025年10月31日

查看全文
  • Liang Zhang,
  • Jiahui Du,
  • Zikai Xie,
  • Linjiang Chen,
  • Wenlang Li,
  • Weihang Geng,
  • Yi Zhou,
  • Xinwen Ou,
  • Chengtao Gong,
  • Yijun Gao,
  • Shan He,
  • Chunxing Yan,
  • Chengbin Zhao,
  • Yantian Jiao,
  • Sheng-Yi Yang,
  • Bing Huang,
  • Jacky W. Y. Lam,
  • Jun Qian,
  • Jun Jiang,
  • Ben Zhong Tang,
  • Hexiang Deng

摘要

The development of porous crystalline materials with targeted properties remains challenging owing to the vast chemical design space and the high cost of experimental screening. Here we develop an artificial-intelligence-assisted interactive experiment–learning evolution approach to accelerate the discovery of highly fluorescent covalent organic frameworks (COFs). This approach integrates model recommendation, experimental validation and active learning in an iterative refinement cycle, allowing the artificial intelligence model to evolve along the process. Among the 520 possible combinations derived from a library of 20 amine and 26 aldehyde building blocks, we needed to experimentally evaluate only 11 COFs to identify one with a remarkable photoluminescence quantum yield of 41.3%. By embedding electronic configuration and quantum-level insights into the learning process, this approach transcends intuition based on statistical analysis intuition to enable material discovery driven by chemical knowledge, enhancing prediction reliability and interpretability. We also reveal the fluorescence mechanism of these COFs and outline the critical role of HOMO–LUMO alignment and excited-state charge distribution.