Magic angle effect driven by deep learning: Giant electromechanical coupling in four-layer twisted lithium niobite

The electromechanical coupling coefficient (kt2) is a key factor in determining the bandwidth of acoustic resonators. Most piezoelectric materials exhibit relatively low coupling coefficients, making it challenging to achieve broader frequency ranges. In this study, we used a deep learning model to explore the magic angle effect in four-layer twisted lithium niobate (LN) and significantly improved kt2. By leveraging deep learning, we can optimize the material’s thickness and twist angles, and accurately predict the resonant and anti-resonant frequencies across different configurations to enhance kt2. Theoretical results demonstrate that, at specific thicknesses and twist angles, the four-layer LN structure achieves a coupling coefficient of up to 90%, outperforming traditional single-crystal and dual-layer twisted LN. This study provides a new perspective at the intersection of materials science and deep learning, highlighting the potential of AI to optimize complex multilayer structures for improved kt2.