Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks
#covalent organic frameworks #photocatalytic #inverse design #hydrolysis trap #durability #agentic workflow #materials science
📌 Key Takeaways
- Researchers developed an agentic workflow for inverse design of durable photocatalytic covalent organic frameworks (COFs).
- The method focuses on escaping the hydrolysis trap, a common degradation pathway for COFs in photocatalytic applications.
- This inverse design approach aims to predict and synthesize COFs with enhanced stability and performance.
- The workflow represents a significant advancement in materials science for sustainable energy and chemical production.
📖 Full Retelling
arXiv:2603.05188v1 Announce Type: cross
Abstract: Covalent organic frameworks (COFs) are promising photocatalysts for solar hydrogen production, yet the most electronically favorable linkages, imines, hydrolyze rapidly in water, creating a stability--activity trade-off that limits practical deployment. Navigating the combinatorial design space of nodes, linkers, linkages, and functional groups to identify candidates that are simultaneously active and durable remains a formidable challenge. Here
🏷️ Themes
Materials Science, Photocatalysis
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Original Source
-- Chemical Physics arXiv:2603.05188 [Submitted on 5 Mar 2026] Title: Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks Authors: Iman Peivaste , Nicolas D. Boscher , Ahmed Makradi , Salim Belouettar View a PDF of the paper titled Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks, by Iman Peivaste and 3 other authors View PDF HTML Abstract: Covalent organic frameworks are promising photocatalysts for solar hydrogen production, yet the most electronically favorable linkages, imines, hydrolyze rapidly in water, creating a stability--activity trade-off that limits practical deployment. Navigating the combinatorial design space of nodes, linkers, linkages, and functional groups to identify candidates that are simultaneously active and durable remains a formidable challenge. Here we introduce Ara, a large-language-model agent that leverages pretrained chemical knowledge, donor--acceptor theory, conjugation effects, and linkage stability hierarchies, to guide the search for photocatalytic COFs satisfying joint band-gap, band-edge, and hydrolytic-stability criteria. Evaluated against random search and Bayesian optimization over a space consisting of candidates with various nodes, linkers, linkages, and r-groups, screened with a GFN1-xTB fragment pipeline, Ara achieves a 52.7\% hit rate (11.5$\times$ random, p = 0.006), finds its first hit at iteration 12 versus 25 for random search, and significantly outperforms BO 0.006). Inspection of the agent's reasoning traces reveals interpretable chemical logic: early convergence on vinylene and beta-ketoenamine linkages for stability, node selection informed by electron-withdrawing character, and systematic R-group optimization to center the band gap at 2.0 eV. Exhaustive evaluation of the full search space uncovers a complementary exploitation--exploration trade-off between the agent and ...
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