A literature-grounded scientific reasoning framework for defect-engineered TiO$_{2}$ photocatalysts

Abstract

Defect-engineered TiO$_2$ photocatalysts are extensively investigated for photocatalytic hydrogen evolution; however, the highly heterogeneous nature of the literature, including inconsistent descriptors, diverse synthesis protocols, non-uniform activity metrics, and incomplete mechanistic reporting, limits the applicability of conventional machine-learning approaches based solely on statistical regression. Here, we present a literature-grounded large language model (LLM)-assisted scientific reasoning framework for defect-engineered TiO$_2$ photocatalysts integrating curated literature data, mechanistic rule extraction, and retrieval-augmented reasoning. A harmonized database was constructed from experimentally relevant publications specifically selected for hydrogen-evolution-related defect engineering in TiO$_2$, covering polymorph-dependent behavior, hydrogenation conditions, Ti$^{3+}$ defect states, oxygen vacancies, illumination conditions, and photocatalytic activity descriptors. In parallel, mechanistic evidence sentences and publications-defined scientific rules were encoded into a structured reasoning layer enabling explainable inference beyond black-box prediction. The resulting framework combines structured experimental descriptors, semantic literature retrieval, and mechanistic interpretation to generate confidence-aware recommendations for optimal defect-engineering conditions. For example, the AI agent identified a consistent optimal anatase hydrogenation window centered at ~500 $°$C under H$_2$-containing atmospheres for approximately 1 h, supported by mechanistic evidence linking balanced Ti$^{3+}$/oxygen-vacancy populations with enhanced photocatalytic hydrogen evolution.