Abstract

Solid-state spin defects in wide-bandgap 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$45,000 thermodynamically stable compounds, we identify 122 high-confidence candidates (confidence $>0.95$), recovering all experimentally verified hosts (C, SiC, ZnO, ZnS) and predicting unexplored materials including TiO$_2$, PbWO$_4$, and layered chalcogenides (HfS$_2$, 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 TiO$_2$ reveal deep, isolated mid-gap 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.

Md Mahshook A

Research Scholar

My research interest includes using machine learning for material property prediction and accelerated materials discovery.

Rudra Banerjee

Assistant Professor, Computational Condensed Matter

Designing next-generation magnetic, catalytic, and quantum materials from first principles — bridging atomic-scale disorder to device-relevant function.