Document Informatics for Scientific Learning and Accelerated Discovery

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