@article{Guo2026, 
author = {Zexu Guo and Hongyu Yang and Hui Qiao and Kui Liu and Zhihan Zhang and Linzhuang Xing and Yihang Li and Zhimin Li and Yue Hao},
title = {Data-driven and interpretable machine learning accelerates discovery of Li4SrCaSi2O8-based microwave ceramics for high-performance dielectric antennas},
year = {2026},
journal = {Journal of Advanced Ceramics},
volume = {15},
number = {3},
pages = {9221257},
keywords = {microwave ceramic, dielectric properties, dielectric antenna},
url = {https://www.sciopen.com/article/10.26599/JAC.2026.9221257},
doi = {10.26599/JAC.2026.9221257},
abstract = {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 Li4SrCaSi1.98Sn0.02O8 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.}
}