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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title:
PyMKS - Materials Knowledge Systems in Python
Abstract:
Community development of materials data analytics toolsets can dramatically alter the landscape of the existing materials innovation ecosystem, and transform the vision outlined in the MGI and ICME documents into reality. The Materials Knowledge Systems framework provides a viable approach for efficient exploration of the unimaginably large materials and process (i.e. manufacturing) design space through the development and implementation of efficient meta-models. Materials Knowledge Systems in Python (PyMKS) aims to seed and nurture an emergent materials analytics user group focused on homogenization and localization linkages central to virtually all multiscale materials modeling efforts. PyMKS provides high level access to the Materials Knowledge Systems framework through simple APIs and leverages open source scientific computing and machine learning packages in Python. An overview of the PyMKS project as well as examples using this Materials Informatics tool will be presented.
Bio:
David Brough is a third-year PhD student in the School of Computational Science and Engineering. As an undergraduate, David studied theoretical physics and researched the evolution of chaotic systems using neural networks at Westminster College. He also earned a Master’s degree in experimental condensed matter physics at Brigham Young University where he fabricated and characterized thin-films used in nanostructured high capacity battery electrodes and in X-ray windows. David’s current research interest is creating materials informatics tools and protocols that efficiently leverage large datasets to learn structure-processing and structure-property relationships.
