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<p>This study presents a Density Functional Theory (DFT) investigation of transition metal oxides for hydrogen storage, employing machine learning as a supplementary screening tool. Given the scarcity of TMO data, we leverage Unsupervised Domain Adaptation to transfer knowledge from metal hydrides to metal oxides. Among the instance-based UDA methods tested, the nearest neighbor weighting with eXtreme Gradient Boost (NNW-XGB) model achieved the largest reduction in distributional discrepancy and predicted hydrogen storage capacities of 3.23 Hwt% for spinel Ca₂TiO₄ and 3.14 Hwt% for perovskite CaVO₃. DFT validation using the PBE0 + Grimme's D3 confirmed that both materials maintain structural and electronic stability up to their saturation limits. The comparison shows reasonable agreement for Ca₂TiO₄ (3.03 Hwt% vs. 3.23 Hwt%) but an overestimation for CaVO₃ (2.11 Hwt% vs. 3.14 Hwt%), attributed to lattice strain effects not captured by composition-based descriptors. Detailed electronic structure analysis via projected density of states (PDOS) and d-band center calculations revealed that hydrogen adsorption is driven by charge transfer from hydrogen to the B-site transition metal (Ti, V), with the progressive filling of d-states leading to electronic saturation at higher coverages. Compositional trend analysis identified promising combinations of A-site (Mg, Be, Ca, Al) and B-site (Sc, Ti, V, Fe) elements.</p>

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Prediction and investigation of transition metal oxides for hydrogen storage application via machine learning-assisted density functional theory

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Romel Mark Irabon,
  • Rustom Hibone,
  • Francis Kirby Burnea,
  • Giovanni Paylaga,
  • Leo Cristobal Ambolode II

摘要

This study presents a Density Functional Theory (DFT) investigation of transition metal oxides for hydrogen storage, employing machine learning as a supplementary screening tool. Given the scarcity of TMO data, we leverage Unsupervised Domain Adaptation to transfer knowledge from metal hydrides to metal oxides. Among the instance-based UDA methods tested, the nearest neighbor weighting with eXtreme Gradient Boost (NNW-XGB) model achieved the largest reduction in distributional discrepancy and predicted hydrogen storage capacities of 3.23 Hwt% for spinel Ca₂TiO₄ and 3.14 Hwt% for perovskite CaVO₃. DFT validation using the PBE0 + Grimme's D3 confirmed that both materials maintain structural and electronic stability up to their saturation limits. The comparison shows reasonable agreement for Ca₂TiO₄ (3.03 Hwt% vs. 3.23 Hwt%) but an overestimation for CaVO₃ (2.11 Hwt% vs. 3.14 Hwt%), attributed to lattice strain effects not captured by composition-based descriptors. Detailed electronic structure analysis via projected density of states (PDOS) and d-band center calculations revealed that hydrogen adsorption is driven by charge transfer from hydrogen to the B-site transition metal (Ti, V), with the progressive filling of d-states leading to electronic saturation at higher coverages. Compositional trend analysis identified promising combinations of A-site (Mg, Be, Ca, Al) and B-site (Sc, Ti, V, Fe) elements.