(231b) A Molecular Workbench for Computational Soft Materials | AIChE

(231b) A Molecular Workbench for Computational Soft Materials


Phelan, F. Jr. - Presenter, National Institute of Standands & Technolog (NIST)
Jeong, C. - Presenter, National Institute of Standards and Technology
Brady, M. - Presenter, National Institute of Standards and Technology

The objective of the Materials Genome Initiative (MGI) is a new R&D paradigm enabling accelerated materials design via computation leading to a shortening of the time and cost needed to bring new materials to market. This new paradigm will be fostered through the creation of national infrastructure for materials data sharing and analysis that supports Integrated Computational Materials Engineering (ICME), an emerging multiscale paradigm for the integrated modeling of materials from the quantum or molecular level, upward in both length and time scale to the continuum scale represented by structural components. An essential element to meeting these objectives for polymers and related soft materials is the development of a coherent and progressive framework for carrying out coarse-grained modeling at mesoscopic length scales between the atomistic and continuum. Such a framework requires meeting the theoretical challenge of describing the representation and interoperability of materials data and the interoperation of modeling systems at multiple length and time scales, as well as the practical challenge of creating a computing environment that realizes its instantiation.

In this seminar, we describe a computational workbench being developed to provide an environment for the integration of coarse-graining algorithms into a hierarchical, multiscale, modeling platform. In the application, the first level of description is atomistic data which contains the highest level of information about the material. Data and models at progressively higher scales are integrated into the environment by taking advantage of the hierarchical and modular environment. The platform is hierarchical in the sense that the native data structure supports a multiscale description of a material starting with atoms through progressively higher levels of coarse-graining. The environment is modular in the sense that it supports the addition of new functionality through Python scripting and compiled C++ libraries as run-time plugins. Together, these features enable algorithms for different levels of coarse-graining to be integrated into the same environment and function together, given proper theoretical basis. The final element of the application is that of reference data. Components of the NIST Materials Data Curator are being integrated into the environment to support ontology based database descriptions allowing users to build progressive, materials reference libraries.


  1. Materials Data and Informatics, http://www.nist.gov/itl/ssd/is/materials-data-and-informatics.cfm

*Official contribution of the National Institute of Standards and Technology; not subject to copyright in the United States.