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<p>Ferroelectric ceramics are promising energy-storage candidates for miniaturizing high-power electronic systems, yet synergistically enhancing energy density and efficiency remains constrained by intricate coupling between chemical compositions and polarization configurations. Achieving high-throughput compositional exploration while solving real-time polarization dynamics is nearly impossible with traditional simulations due to prohibitive computational costs. Here, we propose an inverse design framework integrating a variational generative model with active learning optimization to accelerate the development of ferroelectrics with enhanced energy-storage performance under limited electric fields. By formulating the time-dependent Ginzburg-Landau equation governing domain structure evolution as conditional sampling within model latent space, achieving synergistic optimization of chemistry and polarization configurations. Through four-round closed-loop synthesis, we successfully obtain Bi<sub>0.5</sub>Na<sub>0.5</sub>TiO<sub>3</sub>-based relaxor-ferroelectrics exhibiting exceptional energy density of ~2.3 J cm<sup>-3</sup> and ~80% efficiency at a low field of 200 kV cm<sup>-1</sup>. This work establishes an efficient, generalizable route for the inverse design of next-generation energy-storage dielectric materials.</p>

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Active learning in latent spaces enables rapid inverse design of ferroelectric ceramics for energy storage

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Zhaochen Xi,
  • Zhentao Wang,
  • Changqing Guo,
  • Ke Xu,
  • Weichen Zhao,
  • Zhengqiao Li,
  • Jian Bao,
  • Haowei Zhou,
  • Cong Zou,
  • Houbing Huang,
  • Di Zhou

摘要

Ferroelectric ceramics are promising energy-storage candidates for miniaturizing high-power electronic systems, yet synergistically enhancing energy density and efficiency remains constrained by intricate coupling between chemical compositions and polarization configurations. Achieving high-throughput compositional exploration while solving real-time polarization dynamics is nearly impossible with traditional simulations due to prohibitive computational costs. Here, we propose an inverse design framework integrating a variational generative model with active learning optimization to accelerate the development of ferroelectrics with enhanced energy-storage performance under limited electric fields. By formulating the time-dependent Ginzburg-Landau equation governing domain structure evolution as conditional sampling within model latent space, achieving synergistic optimization of chemistry and polarization configurations. Through four-round closed-loop synthesis, we successfully obtain Bi0.5Na0.5TiO3-based relaxor-ferroelectrics exhibiting exceptional energy density of ~2.3 J cm-3 and ~80% efficiency at a low field of 200 kV cm-1. This work establishes an efficient, generalizable route for the inverse design of next-generation energy-storage dielectric materials.