A pipeline builds a battery research knowledge graph from 189k OpenAlex papers using author vectors weighted by OpenAlex concepts, KeyBERT/ChatGPT keyphrases, authorship position, and recency, then serializes it as RDF linked to Wikidata.
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An ensemble of XGBoost regression models trained on ~2000 hydrides screens ternary A-B-H compositions for high-Tc superconductivity at 100-300 GPa and flags promising systems such as Ca-Ti-H, Li-K-H, and Na-Mg-H.
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Construction of a Battery Research Knowledge Graph using a Global Open Catalog
A pipeline builds a battery research knowledge graph from 189k OpenAlex papers using author vectors weighted by OpenAlex concepts, KeyBERT/ChatGPT keyphrases, authorship position, and recency, then serializes it as RDF linked to Wikidata.
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Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset
An ensemble of XGBoost regression models trained on ~2000 hydrides screens ternary A-B-H compositions for high-Tc superconductivity at 100-300 GPa and flags promising systems such as Ca-Ti-H, Li-K-H, and Na-Mg-H.