ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature

By Matthew C. Swain, Jacqueline M. Cole. Journal of Chemical Information and Modeling, 2016

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Recent advances and applications of deep learning methods in materials science (2021, methodology): “org [303] ChemicalTagger https://github.”
Machine learning for heterogeneous catalyst design and discovery (2018, background): “ics, particle size, operating temperature, and synthesis conditions.(68,119) Being able to extract large amounts of catalyst”
Extracting accurate materials data from research papers with conversational language models and prompt engineering (2023, methodology): “Many databases have been created using natural language processing (NLP) and language models (LMs) [2– 24 ].”