Document Informatics for Scientific Learning and Accelerated Discovery

Abstract This chapter presents a concept paper that describes methods to accelerate new materials discovery and optimization, by enabling faster recognition and use of important theoretical, computational, and experimental information aggregated from peer-reviewed and published materials-related scientific documents online. To obtain insights for the discovery of new materials and to study about existing materials, research and development scientists and engineers rely heavily on an ever-growing number of materials research publications, mostly available online, and that date back many decades. So, the major thrust of this concept paper is the use of technology to (i) extract “deep” meaning from a large corpus of relevant materials science documents; (ii) navigate, cluster, and present documents in a meaningful way; and (iii) evaluate and revise the materials-related query responses until the researchers are guided to their information destination. While the proposed methodology targets the interdisciplinary field of materials research, the tools to be developed can be generalized to enhance scientific discoveries and learning across a broad swathe of disciplines. The research will advance the machine-learning area of developing hierarchical, dynamic topic models to investigate trends in materials discovery over user-specified time periods. Also, the field of image-based document analysis will benefit tremendously from machine learning tools such as the use of deep belief networks for classification and text separation from document images. Developing an interactive visualization tool that can display modeling results from a large materials network perspective as well as a time-based perspective is an advancement in visualization studies.