Machine-learning-guided inverse design of lead-free relaxors enabled by multimodal literature mining

Lead-free relaxors combining high permittivity with thermal robustness are essential for integrated electronics and power systems operating at elevated temperatures. However, their development remains fundamentally hindered by inherent property trade-offs and the immense chemical search space. Here, we report an inverse-design framework integrating multimodal literature mining with physics-guided descriptor-based deep ensemble learning for systematic relaxor screening. By screening a combinatorial space exceeding 150 million candidates, we identify the (Sr_0.48Na_0.26Bi_0.26)(Ti_1-xSn_x)O₃ (0 ≤ x ≤ 0.02) compositions as a low-complexity composition window that satisfies a sparsely populated tri-target dielectric-property regime. Experimental validation confirms that compositions with x = 0.01 and 0.02 meet the predefined design targets for dielectric stability while maintaining a room temperature permittivity above 3300. This stability is further reflected in the simultaneous satisfaction of the upper-temperature-side X5R/X6R/X7R criteria. Atomic-scale characterization reveals that octahedral framework expansion induces polar heterogeneity, underlying the observed dielectric performance. Our results establish an experimentally validated down-selection framework for navigating complex relaxor design spaces and accelerating the data-driven discovery of temperature-stable lead-free dielectrics.