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.