Solid-state spin defects in wide-band-gap semiconductors are leading candidates for quantum information processing, but systematic identification of suitable host materials remains limited by the cost of first-principles screening across vast chemical spaces. We address this with a composition-only machine learning framework built on heterogeneous Rashomon set ensembles: by contrasting the feature attributions of seven diverse classifiers, we extract consensus design rules that no single model identifies alone—filled valence $s$-, $d$-, and $f$ shells, low chemical heterogeneity, and enrichment in C, S, Si, and O favor quantum compatibility. Screening $\approx 45000$ thermodynamically stable compounds, we identify 122 high-confidence candidates (confidence $>0.95$), recovering most experimentally verified hosts (C, $\mathrm{SiC}, \mathrm{ZnO}$, and $\mathrm{ZnS}$) and predicting unexplored materials including ${\mathrm{TiO}}_2, {\mathrm{PbWO}}_4$, and layered chalcogenides (${\mathrm{HfS}}_2, {\mathrm{ZrS}}_2$). Density-functional perturbation theory calculations on 12 representative materials validate dielectric screening as a coherence proxy ($R^2=0.89$ against experimental $T_2$), and vacancy calculations for ${\mathrm{TiO}}_2$ reveal deep, isolated midgap states favorable for spin-defect hosting. The framework provides transferable, physically grounded design principles for rational quantum materials discovery beyond traditional carbide and nitride hosts.

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• NV centers
• Quantum coherence & coherence measures
• Quantum information with solid state qubits
• Qubits
• First-principles calculations
• Machine learning
• Materials modeling