High-performance dielectric antennas have pursued a high quality factor (Q×f) for microwave ceramics. Nevertheless, the cross-laboratory inconsistency in reported Q×f would confuse the invention of materials systems, owing to divergent preparation and measurement protocols. Herein, based on a self-consistent dataset, an interpretable machine learning framework is proposed to unveil the structure–property relationship and consequently guide the compositional design of candidate microwave ceramic Li₄SrCaSi₂O₈. Through feature engineering, nine critical features are identified, in which the Si/Li atomic mass ratio (Si/Li-AW), Si/Sr ionic radius ratio (Si/Sr-IR), and total electronegativity of cations (TEC) are found to be predominant. Interpretability technologies further reveal that a higher Si/Li-AW coupled with lower Si/Sr-IR and TEC is conducive to the increase in the Q×f value for the chosen decision tree (DT) model. Guided by these insights, Sn⁴⁺-doped microwave ceramic Li₄SrCaSi_1.98Sn_0.02O₈ is created with a Q×f value up to 83,526 GHz, the origin of which is elucidated by P–V–L theory combined with first-principles calculations and infrared spectroscopy. Such an optimized material is ultimately verified by a microstrip patch antenna with a high radiation efficiency of 81.12% and a gain of 5.94 dB in the C-band.

It is challenging to theoretically predict the coefficient of thermal expansion (CTE) for binary A_mB_n crystals owing to the complexity of their crystal structures and computational procedures. Herein, the Pearson feature selection method is utilized to identify nine key features associated closely with crystal structures, and a back-propagation neural network model with two hidden layers containing 24 and 15 neurons is adopted to achieve the optimal matching effect of the CTE, which is specifically optimized by the pelican optimization algorithm. Moreover, the black-box nature of the model is well elucidated by interpretability techniques of Shapley additive explanations (SHAP) and accumulated local effects (ALE), including the specific impact rules of each feature and the interaction effects between features on the CTE. It is found that the feature of average bond length contributes up to 27%, while low-influence features serve an important function in increasing prediction accuracy. The findings demonstrate the high efficiency and accuracy of the developed model for predicting the CTE of binary crystals.

Pb(Zr,Ti)O₃-based ceramics are the mainstream materials for commercial multilayer piezoelectric ceramic actuators, but to date, large strains at low electric fields have not been well solved. Herein, 0.95Pb(Zr_0.56Ti_0.44)O₃–0.05(Bi_0.5Na_0.5)TiO₃–xBaZrO₃ (PZT–BNT–xBZ) ceramics with efficient ferroelectric domain wall motion were designed and realized by reducing lattice distortion and changing the domain structure. It is found that the introduction of BaZrO₃ (BZ) weakens the tetragonal phase distortion of PZT, contributing to a reduction in the mechanical stress that impedes the migration of domain walls. Moreover, the domain structures could be modified by adjusting the BZ content, where short and broad striped domains are constructed with high amplitude characteristics to enhance the domain wall motion. A large strain of 0.39% is accordingly achieved at an electric field as low as 40 kV/cm for the sample with x = 0.03, accompanied by excellent temperature stability over the temperature range of 30–210 °C. This study delves into the synergistic effects of reducing lattice distortion and changing domain structure on domain wall motion and provides an effective strategy to improve the strain of PZT-based piezoelectric ceramics.