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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Uncertainty-aware RL for chemical language models raises true hit rate from 0.5 to 0.75 by favoring low-uncertainty regions during optimization.
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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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Uncertainty-aware reinforcement learning for chemical language models
Uncertainty-aware RL for chemical language models raises true hit rate from 0.5 to 0.75 by favoring low-uncertainty regions during optimization.