ArticleNature communications2026
Active learning in latent spaces enables rapid inverse design of ferroelectric ceramics for energy storage.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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