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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2606.01089 |
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| _version_ | 1866916070925795328 |
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| author | Dominguez-Gutierrez, F. J. Wierzbicka, E. |
| author_facet | Dominguez-Gutierrez, F. J. Wierzbicka, E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01089 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A literature-grounded scientific reasoning framework for defect-engineered TiO$_{2}$ photocatalysts Dominguez-Gutierrez, F. J. Wierzbicka, E. Materials Science 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. |
| title | A literature-grounded scientific reasoning framework for defect-engineered TiO$_{2}$ photocatalysts |
| topic | Materials Science |
| url | https://arxiv.org/abs/2606.01089 |