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We introduce state-aligned graph-wavelet fingerprints to jointly resolve electronic states and network responses for charge trap classification in amorphous materials, applying them to electron trapping in a-Si$_3$N$_4$. By aligning trap states before and after electron localization across 415 neutral/charged cell pairs, we connect electronic identity to multiscale structural fingerprints, revealing that charge-driven conversion significantly reorients the graph structure and increases fingerprint amplitude. This method quantifies site transformation, tracks localization gain via level shifts, and identifies charge-induced defect formation, particularly at sites with Si-N bond breaks, as a dominant pathway for deep spectral traps.
Charge-induced defect formation, often involving Si-N bond breaks, accounts for a significant portion of deep spectral traps in amorphous silicon nitride, as revealed by novel state-aligned graph-wavelet fingerprints.
Classifying charge traps in amorphous materials requires the electronic state and the network response to be resolved together. We develop state-aligned graph-wavelet fingerprints and apply them to electron trapping in a-Si$_3$N$_4$, where intrinsic-polaronic trapping, coordination-driven trapping, and charge-driven conversion coexist within one ensemble. Across 415 pairs of relaxed neutral and negatively charged cells, alignment of the trap state before and after localisation connects electronic identity to multiscale structural fingerprints. Charge-driven conversion produces substantial reorganisation across graph scales and increases the fingerprint amplitude. The fingerprint change identifies the transformation each site undergoes, while the occupation-induced level shift tracks localisation gain and graph deformation tracks stabilisation. Charge-induced defect formation dominates the deepest spectral-trap decile: 69\% form at sites whose Si--N bond breaks on capture, although this route accounts for 44.2\% of the classified traps. The fingerprints unify electronic identity, multiscale network reorganisation and energetic stabilisation in a quantitative framework for resolving charge trapping in amorphous materials.