Akademik Veri Yönetim Sistemi
Materials and Design, cilt.267, 2026 (SCI-Expanded, Scopus)
Hydrogen is a promising energy carrier for decarbonized technologies, but reversible on-board storage remains challenging because efficient uptake and release under near-ambient conditions require a narrow adsorption-energy window. Here, we combine high-throughput electronic-structure calculations with explainable machine learning to uncover the governing factors of molecular hydrogen adsorption on single-atom-doped TiO2 nanoparticles. Screening 30 dopants across perpendicular and parallel H2 adsorption geometries reveals substantial variation in adsorption strength and clear configuration-dependent behavior. From a broad descriptor space, a three-stage feature-selection strategy identifies eight robust predictors, which are further reduced through symbolic regression to an analytical and interpretable model. The resulting descriptor space is governed by H–H activation, dopant–H2 distance, and d-electron rearrangement. Adsorption within the target window is associated with balanced σ-donation and π-back-donation, with Group 4 and 5 transition metals emerging as the most robust candidates across geometries. Combined with Langmuir-based desorption estimates, these results provide a mechanism-based screening rule for reversible hydrogen storage on doped TiO2.