This tutorial presents novel computational analytic methods capable of unlocking the human knowledge that’s been documented and archived in the unstructured text of hundreds of millions of scientific publications to extend scientific discovery beyond human capacity, and ways to automate experimental knowledge generation. The instructors will explore pathways for visualizing and comprehending knowledge propagation, evolution, and assessment of scientific research fronts, data-based hypothesis generation and methods for quantifying research impact within the scientific community and beyond.
In Part Two of this three-part tutorial, Rama Vasudevan introduces the arguments for the need for text analysis in the field of materials growth, and focuses on a specific use case of text mining of papers on epitaxial thin films of complex oxides, for determination of growth conditions-functional property relationships. An open source annotation tool is modified for this purpose, using regular expressions on text from selected papers to automatically annotate text associated with growth conditions and functional properties. Via the use of crowd sourcing, the annotations are checked and matched with the materials of interest, to populate a database containing information on the type of material grown, the substrate, growth conditions and functional property information. The methods shown here are general, and can be applied to a wide variety of growth methods and material types.