ArticleNature chemistry2025
Discovery of highly fluorescent covalent organic frameworks through AI-assisted iterative experiment-learning cycles.
Article in Nature chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Rapid and Green Extraction of Food Contaminants: Integrating Advanced Porous Materials and Miniaturized Functional Platforms for Chromatographic and Mass Spectrometric Analysis.Comprehensive reviews in food science and food safety · 2026Review
- A cuproptosis-inducing covalent organic framework synergizes with oncolytic virus for tumor radiotherapy.Nature communications · 2026Article
- Luminescent Covalent-Organic Frameworks: Design Strategies, Photophysical Mechanisms, and Applications.Small methods · 2026Review
- Membrane-Confined Proton Management Orchestrates Proton-Electron Transfer for Efficient Photocatalytic Hydrogen Peroxide Synthesis.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research.JACS Au · 2026Review
- Highly flexoelectric response for uranium removal and recovery using the organic-inorganic hybrid material.Nature communications · 2026Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
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.
Identifiers
41177840What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.