Machine learning on the largest curated alkali-activated slag dataset shows that average metal oxide dissociation energy serves as a compact, physically interpretable reactivity descriptor enabling strength prediction and low-emission design space exploration.
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cond-mat.mtrl-sci 2years
2026 2verdicts
UNVERDICTED 2roles
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Na-rich KNN thin films on 8-inch silicon wafers reach d33f of 79 pm/V and e31f of 10 C/m2 by adopting a monoclinic phase that suppresses pyrochlore and segregation.
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Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag
Machine learning on the largest curated alkali-activated slag dataset shows that average metal oxide dissociation energy serves as a compact, physically interpretable reactivity descriptor enabling strength prediction and low-emission design space exploration.
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The KNN rollercoaster: from bulk ceramics to phase engineered wafer-scale thin films
Na-rich KNN thin films on 8-inch silicon wafers reach d33f of 79 pm/V and e31f of 10 C/m2 by adopting a monoclinic phase that suppresses pyrochlore and segregation.