Electronic Devices Tran Trung Duy 15 2 2026 https://doi.org/10.6025/ed/2026/15/2/104-126 https://www.dline.info/ed/fulltext/v15n2/edv15n2_3.pdf This study investigates the discriminatory power of hierarchical atomic, crystal/structural, and energetic descriptors for classifying semiconductor materials into three Electronic Property Vector (EPV) categories: Band Gap, Conduction Band Minimum (CBM), and Valence Band Maximum (VBM). Using a structured dataset of 9,700 semiconductor records, the analysis employs one way ANOVA with effect size measures (²), mut ual-information scoring, stratified cross validated hierarchical classification, Pearson correlation network analysis, and principal component analysis (PCA). Univariate results reveal that no individual descriptor achieves meaningful class separation; the strongest energetic descriptor, DOS Peak Value, accounts for only 0.11% of EPV variance (² = 0.00109, p = 0.005), while all other descriptors yield smaller or non significant effects. Hierarchical multi class classification across seven progressively combined descriptor blocks produces accuracies ranging from 33.09% to 34.87%, representing gains of at most 1.5 percentage points above the 33.3% random chance baseline. The best configuration (Atomic + Energetic) attains a Macro F1 of only 0.346. Correlation network and PCA analyses demonstrate that the four energetic descriptors are nearly orthogonal (maximum |r|  0.021) and span a genuinely four dimensional feature space with near uniform variance distribution. These findings establish that the currently available low level descriptors provide only marginal discriminatory information for EPV classification. The results underscore the necessity for higher order, interaction rich, or graph based representations to achieve reliable semiconductor electronicproperty classification.