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.
Machine Learning in Concrete Science: Applications, Challenges, and Best Practices
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AI tools deliver useful overviews for research exploration but prove unreliable for precise information extraction and systematic reviews due to low explainability, reproducibility, and transparency.
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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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Useful for Exploration, Risky for Precision: Evaluating AI Tools in Academic Research
AI tools deliver useful overviews for research exploration but prove unreliable for precise information extraction and systematic reviews due to low explainability, reproducibility, and transparency.