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<p>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<sub>0.48</sub>Na<sub>0.26</sub>Bi<sub>0.26</sub>)(Ti<sub>1−x</sub>Sn<sub>x</sub>)O<sub>3</sub> (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 <i>x</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Kwanwoo Song,
  • Youngmin Kim,
  • Jaehyun Kim,
  • Byeong-Jae Min,
  • Hyun-Cheol Song,
  • Nayeon Kang,
  • Jungho Ryu,
  • Ho Won Jang

摘要

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 (Sr0.48Na0.26Bi0.26)(Ti1−xSnx)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.