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This study presents a novel NLP pipeline tailored for the extraction of luminescent materials data from scientific literature, addressing challenges in parsing and recognizing domain-specific chemical formulas. The pipeline successfully extracted 6,400 material-emission relationships from 16,659 papers, enabling a machine learning model to predict emission wavelengths with high accuracy. Experimental validation of synthesized phosphors confirmed the effectiveness of the data extraction and prediction workfl

Author

Tina P

Language

EN