Knowledge-Guided Autonomous Discovery of Microenvironment-Tuned Metal-Organic Framework Photocatalysts

Designing second-sphere microenvironments that promote proton-coupled electron transfer is central to catalysis yet difficult to achieve in porous solids, such as metal-organic frameworks (MOFs). Here, we report an end-to-end workflow that couples literature-guided large-language-model (LLM) reasoning with real-time experimental feedback to propose, test, and refine microenvironment designs in MOF photocatalysts. The system mined and fused three domains (namely, photocatalytic H2 production, hydrogenases and enzyme-mimetic catalysis) and deduced the hypothesis that placing basic, hydrogen-bonding groups near catalytic centers would facilitate water activation and proton transfer. The hypothesis was instantiated by postsynthetic modification of UiO-67, generating 31 Pt@UiO-67-X variants and evaluating them across six closed-loop iterations on an automated platform. The search converged on Pt@UiO-67-30 (8-quinolinecarboxylic acid), which delivered 2.33 mmol g-1 h-1, a ∼36-fold improvement over the parent material; in a larger, optimally illuminated reactor the same catalyst reached 12.48 mmol g-1 h-1 while preserving the library's rank order. Photoluminescence quenching, enhanced photocurrent, and reduced impedance are consistent with faster charge separation, and first-principles calculations are consistent with reduced proton-transfer barriers via N···H hydrogen-bond networks. These results establish a practical microenvironment-engineering strategy in MOFs and show how LLM-guided knowledge fusion with experiment-in-the-loop reasoning can systematize and accelerate targeted discovery of functional materials.