{"292551":{"#nid":"292551","#data":{"type":"event","title":"The Materials Data Scientist and the Space In-Between","body":[{"value":"\u003Cp\u003EDr. Tony Fast leads this Chalk \u0026amp; Talk discussion. Isolated improvements in algorithms, technology, and computation directly impact the landscape of information use in materials science. \u0026nbsp;A grander adoption of data-driven science is looming within materials science, as the materials science community rapidly generates large volumes of heterogeneous materials information. \u0026nbsp;These multi-modal and spatiotemporal packets of information are quickly outgrowing traditional empirical approaches to analysis. \u0026nbsp;This provides a new opportunity to create research profiles of users steeped in materials domain knowledge and data science; these users will interface data scientists with materials scientists to fill the space in-between.\u003C\/p\u003E\u003Cp\u003EThe materials data scientist will use a diverse set of skills for data management, feature identification, feature representation\/parametrization, data modeling, and reporting. The materials data scientist will understand reduced order modeling, statistics, algorithms, and coding. \u0026nbsp;The materials data scientist will also need to be social in order to \u0026nbsp;enable the next generation of bi-directional structure-property-processing linkages that are required to discover and develop advanced materials.\u003C\/p\u003E\u003Cp\u003EIn this talk, a few early case studies in data-driven methods for solving materials science problems will be explored. \u0026nbsp;Emerging spatial statistics tools will be explored that enable an objective comparison of static and evolving 3-D material volumes from molecular dynamics simulation, micro-CT, and Scanning Electron Microscopy. \u0026nbsp;Also, the statistics will provide a foundation to create improved bottom-up homogenization relationships in fuel cell materials. \u0026nbsp;Lastly, applications of the Materials Knowledge System, a data-driven meta-model to create top-down localization relationships will be explored for phase field model and finite element model information. \u0026nbsp;Lastly, this lecture will touch on how to ensure subsequent generations of materials scientists are increasingly collaborative.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Dr. Tony Fast will explore several early case studies in data-driven methods for solving materials science problems."}],"uid":"27869","created_gmt":"2014-04-22 11:58:25","changed_gmt":"2017-04-13 21:22:40","author":"Allison Caughey","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2014-04-24T17:30:00-04:00","event_time_end":"2014-04-24T19:00:00-04:00","event_time_end_last":"2014-04-24T19:00:00-04:00","gmt_time_start":"2014-04-24 21:30:00","gmt_time_end":"2014-04-24 23:00:00","gmt_time_end_last":"2014-04-24 23:00:00","rrule":"RRULE:FREQ=DAILY;INTERVAL=1;UNTIL=20140425T035959Z;WKST=SU","timezone":"America\/New_York"},"extras":[],"groups":[{"id":"217141","name":"Georgia Tech Materials Institute"}],"categories":[],"keywords":[{"id":"39591","name":"computational modeling"},{"id":"92021","name":"data sciences"},{"id":"2294","name":"materials science"},{"id":"167914","name":"structure-property-processing"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78751","name":"Undergraduate students"},{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EHolly Rush\u003Cbr \/\u003E\u003Ca href=\u0022mailto:holly@cc.gatech.edu\u0022\u003Eholly@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}