ArticleNature communications2026
Machine-learning-guided inverse design of lead-free relaxors enabled by multimodal literature mining.
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
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
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