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SPEAR (Structure Property Explainability with Attention Regularization) provides a principled training constraint for explainable structure property regression, yielding mechanistically meaningful explanations without sacrificing predictive performance.
High-throughput synthesis and characterization of novel ceramic materials with improved thermomechanical properties and phase stability are needed to accelerate the discovery of next-generation thermal barrier materials. A combinatorial thin film material library of (GdDyHoEr)2Zr2O7 were created via combinatorial magne...
An integrated workflow that combines document-grounded large language model (LLM) literature mining, human verification, and probabilistic modeling to enable uncertainty-aware prediction of the coefficient of thermal expansion (CTE) in complex oxides is introduced.
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