AI-Assisted Discovery of High-Performance Dielectric Materials for 5G and 6G Communication Components
Keywords:
artificial intelligence; dielectric materials; 5G; 6G; materials informatics; microwave ceramics; multilayer ceramic capacitors; sustainabilityAbstract
The rapid evolution of fifth-generation and sixth-generation communication systems is creating urgent demand for dielectric materials that can simultaneously satisfy stringent high-frequency electrical performance, thermal stability, mechanical processability, and manufacturing scalability. Traditional empirical methods for discovering such materials are increasingly insufficient because the relevant composition and processing spaces are vast and highly coupled. Artificial intelligence assisted discovery offers a pathway to navigate this complexity through data-driven modeling, active learning, and high-throughput computational screening. However, translating algorithmic predictions into practical communication components requires a systems-level perspective that extends beyond predictive accuracy. This paper examines the structural trade-offs, data infrastructures, experimental validation architectures, governance mechanisms, and sustainability considerations that shape AI-assisted dielectric materials discovery for 5G and 6G components. It discusses how dielectric constant, loss behavior, temperature stability, and integration compatibility form a multi-objective design space that cannot be addressed by materials chemistry alone. The analysis connects machine learning advances with microwave ceramics, polymer dielectrics, multilayer capacitor processing, and electromagnetic component integration. The paper further considers fairness in data access, critical raw material supply, lifecycle environmental impact, and dual-use policy implications. By treating materials discovery as an integrated socio-technical system, the paper offers forward-looking insights into how artificial intelligence can support resilient and sustainable communication infrastructures without ignoring institutional and governance constraints.
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