Open Source Web Framework for Chemical-Physics Simulations, Data, and Analytics

Period of Performance: 01/01/2015 - 12/31/2015

$150K

Phase 1 SBIR

Recipient Firm

Kitware
28 Corporate Drive Array
Clifton Park, NY 12065
Firm POC
Principal Investigator

Abstract

The Materials Genome Initiative, in which DOE is participating through its Basic Energy Sciences program, aims to \support US institutions in the effort to discover, manufacture, and deploy advanced materials twice as fast at a fraction of the cost & quot;. This can only be achieved by making significant improvements to the way that we use predictive capabilities to go from initial concept to manufactured and deployed materials. The way that scientists integrate, analyze, use, store and share diverse materials science datasets will be revolutionized by developing a scalable, extensible web framework. The technical approach will leverage best-in-class open-source technologies, and develop new approaches strategically to build an open source web framework for chemical-physics simulations, data and analytics. Features will be developed using a scripting language to glue components together on the server-side, with compiled programs providing optimized implementations and access to existing approaches. The client application will build upon existing frameworks to provide a rich, responsive experience using modern web technologies. The Phase I prototype will include basic structure editing, scheduling of computational simulations, ingestion of the results, and addition of pertinent information to the triple store. Data will be stored in a pragmatic form, and interfaces for defining more complex concepts to pose questions about the data will be explored, including support for natural language queries, definition of operations performed on results to other exploration of correlations, e.g. solar energy conversion efficiency correlated with chemical concepts such as number of rings, structural similarities, substructure matches, etc. The integration of experimental data to augment predictive capabilities of in-silico simulations will be demonstrated, offering both simple analyses and more complex approaches employing specialized languages. This project will leverage investments made by the DOE and other agencies to provide a powerful web application and software framework for the materials design community. It will create a software platform that can serve as a reference for the community. The use of permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close yet make it available to the wider linked semantic web.