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Author

Nida Katı

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Open access Aug 2026

Source-aware materials informatics for biomass-derived carbon electrodes: predicting supercapacitor capacitance from precursor and process descriptors

Biomass-derived porous carbons are promising electrode materials for sustainable electrochemical energy storage, yet their capacitance depends on coupled precursor and process variables that are difficult to optimize experimentally. Here, we present a source-aware materials-informatics framework for predicting the spec...

F. Uçar, Nida Katı, Mohammed Abduljalil Saeed Ahmed · 0 citations
Open access Aug 2026

Interpretable machine learning for Curie temperature prediction of magnetic materials: compositional descriptors, shap analysis, and a perovskite case study

The Northeast Materials Database is leveraged to develop machine learning models that predict magnetic materials with targeted Curie temperatures from composition-derived descriptors rooted in molecular-level elemental properties, supplemented by a small set of coarse crystal-system and structure-family indicators.

F. Uçar, Nida Katı · 0 citations

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