Physics-Informed Synthetic Dataset for Antifungal and Surface Performance Prediction in Sol–Gel TiO₂–SiO₂–ZnO Wood Coatings

This dataset contains 120 physics-informed synthetic records representing experimental conditions for sol-gel-derived TiO2, SiO2, and ZnO wood coating systems. Features include material descriptors (wood species, density, moisture content, oxide weight percentages), process parameters (sol-gel pH, curing temperature, film thickness, UV exposure time), and surface properties (contact angle, roughness). Target variables are antifungal efficiency (%) and mass loss (%). Generative equations encode physicochemical relationships documented in peer-reviewed literature. Dataset was used to train and evaluate five supervised machine learning models. Companion code available at https://github.com/prathimabandaru176-boop/wood-coating-ml-dataset