Data-Driven Screening of High-Performance Interconnect Materials: Integrating Graph Learning with Engineering Safety Constraints

The accelerated design of next-generation semiconductor interconnects faces a critical “applicability gap”. Purely data-driven models effectively navigate vast chemical spaces, but they often yield candidates that are theoretically performant yet violate practical manufacturing constraints. To bridge this disconnect, this study proposes a neuro-symbolic decision support framework that systematically integrates inductive graph learning with deductive engineering logic for Safe-by-Design material screening. The framework operates through a hierarchical dual-stream architecture. First, an inductive Graph Neural Network (GNN) engine transforms 3D crystal structures into topological graph representations to predict thermodynamic stability and metallicity with high discriminative power (AUC = 0.868). Second, a deductive safety layer enforces explicit domain ontology, including toxicity thresholds, raw material costs, and reactivity limits, to preemptively prune high-risk candidates. Operationally, this hybrid approach reduces the candidate search space of over 20,000 compounds by approximately 96% within minutes, demonstrating orders-of-magnitude computational efficiency gains over traditional ab initio high-throughput screening. The system’s reliability is further validated through structural perturbation analysis and high-fidelity physics simulations, identifying robust binary compounds such as HfB and NbAl₃ that exhibit cohesive energies up to 2.1 times that of copper. These results demonstrate the efficacy of integrating symbolic reasoning with deep learning to create transparent, reliability-aware computational tools for early-stage engineering decision-making.