Escaping the hydrolysis trap: a react agent for inverse design of durable photocatalytic covalent organic frameworks [* Cross-Reference *]

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Iman Peivaste; Nicolas D. Boscher; Ahmed Makradi; Salim Belouettar, 2026, "Escaping the hydrolysis trap: a react agent for inverse design of durable photocatalytic covalent organic frameworks [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.5MECA8, LIST Open Repository, V1
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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 we introduce Ara , a large-language-model (LLM) 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 (BO) 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 × random, p = 0.006), finds its first hit at iteration 12 versus 25 for random search, and significantly outperforms BO ( p = 0.006). Inspection of the agent’s reasoning traces reveal interpretable chemical logic: early convergence on vinylene and β -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 BO, suggesting that hybrid strategies may combine the strengths of both approaches. These results demonstrate that LLM chemical priors can substantially accelerate multi-criteria materials discovery. (2026-06-03)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1038/s41524-026-02168-w for the original and latest version of the publication*** (2026-07-01)
Subject Earth and Environmental Sciences; Physics
Keyword Chemical space, Inverse, Convergence (economics), Bayesian optimization, Node (physics), Stability (learning theory), Covalent bond, Space (punctuation), Hydrolysis
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