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Most of the knowledge in materials science literature is in the form ofunstructured data such as text and images. Here, we present a frameworkemploying natural language processing, which automates text and imagecomprehension and precision knowledge extraction from inorganic glasses'literature. The abstracts are automatically categorized using latent Dirichletallocation (LDA), providing a way to classify and search semantically linkedpublications. Similarly, a comprehensive summary of images and plots arepresented using the 'Caption Cluster Plot' (CCP), which provides direct accessto the images buried in the papers. Finally, we combine the LDA and CCP withthe chemical elements occurring in the manuscript to present an 'Elementalmap', a topical and image-wise distribution of chemical elements in theliterature. Overall, the framework presented here can be a generic and powerfultool to extract and disseminate material-specific information oncomposition-structure-processing-property dataspaces, allowing insights intofundamental problems relevant to the materials science community andaccelerated materials discovery.

Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing