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Abstract Protonic ceramic electrochemical cells (PCECs) represent a promising class of solid‐state energy conversion devices capable of high‐efficiency hydrogen production and power generation. However, the practical deployment of planar PCECs is fundamentally constrained by severe structural deformation and mechanical failure during fabrication, stemming from asymmetric shrinkage between the thin electrolyte and the thick NiO‐based support layer. In this work, a functionally integrated, symmetry‐engineered double‐sided electrolyte (DE) design is unveiled, which not only suppresses thermally induced curvature but also unlocks significant gains in electrochemical performance and stability. This architecture intrinsically balances shrinkage dynamics across the cell bilaterally, enabling the fabrication of ultra‐flat 5 × 5 cm²cells with sub‐100 µm thickness variation. A numerical solid mechanics simulation is introduced to investigate and interpret this achievement. Beyond structural advantages, the DE configuration enhances the cell operational stability, delivering a low open‐circuit voltage degradation of 9.5 mV/100 h across 80 thermal cycles. This work establishes a compelling paradigm wherein architectural symmetry directly translates to both mechanical fidelity and functional enhancement, offering a promising route toward PCECs scale‐up. 

Abstract Self‐supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. However, most of the masking‐based pre‐trained language models are not designed for generative design, and their black‐box nature makes it difficult to interpret their design logic. Here a Blank‐filling Language Model for Materials (BLMM) Crystal Transformer is proposed, a neural network‐based probabilistic generative model for generative and tinkering design of inorganic materials. The model is built on the blank‐filling language model for text generation and has demonstrated unique advantages in learning the “materials grammars” together with high‐quality generation, interpretability, and data efficiency. It can generate chemically valid materials compositions with as high as 89.7% charge neutrality and 84.8% balanced electronegativity, which are more than four and eight times higher compared to a pseudo‐random sampling baseline. The probabilistic generation process of BLMM allows it to recommend materials tinkering operations based on learned materials chemistry, which makes it useful for materials doping. The model is applied to discover a set of new materials as validated using the Density Functional Theory (DFT) calculations. This work thus brings the unsupervised transformer language models based generative artificial intelligence to inorganic materials. A user‐friendly web app for tinkering materials design has been developed and can be accessed freely atwww.materialsatlas.org/blmtinker. 

A PCO catalyst was coated onto an LSCF scaffold to enhance the steam tolerance of the air electrode in a high-humidity environment. 

A redox-reversible BLFMN material demonstrates good performance and coking resistance as the fuel electrode in SOFCs.