The rapid development and application of machine learning (ML)techniques in materials science have led to new tools for machine-enabled andautonomous/high-throughput materials design and discovery. Alongside, efforts toextract data from traditional experiments in the published literature with naturallanguage processing (NLP) algorithms provide opportunities to developtremendous data troves for these in silico design and discovery endeavors. WhileNLP is used in all aspects of society, its application in materials science is still in thevery early stages. This perspective provides a case study on the application of NLPto extract information related to the preparation of organic materials. We present the case study at a basic level with the aim todiscuss these technologies and processes with researchers from diverse scientific backgrounds. We also discuss the challenges faced inthe case study and provide an assessment to improve the accuracy of NLP techniques for materials science with the aid ofcommunity contributions.
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