Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.05188v1</id>\n    <title>Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks</title>\n    <updated>2026-03-05T13:57:56Z</updated>\n    <link href='https://arxiv.org/abs/2603.05188v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.05188v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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$\\times$ 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 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 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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='physics.chem-ph'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cond-mat.mtrl-sci'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='physics.comp-ph'/>\n    <published>2026-03-05T13:57:56Z</published>\n    <arxiv:primary_category term='physics.chem-ph'/>\n    <author>\n      <name>Iman Peivaste</name>\n    </author>\n    <author>\n      <name>Nicolas D. Boscher</name>\n    </author>\n    <author>\n      <name>Ahmed Makradi</name>\n    </author>\n    <author>\n      <name>Salim Belouettar</name>\n    </author>\n  </entry>"
}