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In this work, we present the ChemNLP library that can be used for 1) curatingopen access datasets for materials and chemistry literature, developing andcomparing traditional machine learning, transformers and graph neural networkmodels for 2) classifying and clustering texts, 3) named entity recognition forlarge-scale text-mining, 4) abstractive summarization for generating titles ofarticles from abstracts, 5) text generation for suggesting abstracts fromtitles, 6) integration with density functional theory dataset for identifyingpotential candidate materials such as superconductors, and 7) web-interfacedevelopment for text and reference query. We primarily use the publiclyavailable arXiv and Pubchem datasets but the tools can be used for otherdatasets as well. Moreover, as new models are developed, they can be easilyintegrated in the library. ChemNLP is available at the websites:https://github.com/usnistgov/chemnlp and https://jarvis.nist.gov/jarvischemnlp.

ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data