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VIRTUAL - CEI Interdisciplinary Seminar: Kristin Persson, Lawrence Berkeley National Laboratory (LBNL)
VIRTUAL - CEI Interdisciplinary Seminar: Kristin Persson, Lawrence Berkeley National Laboratory (LBNL)
WhenThursday, Apr 23, 2020, 4 – 5 p.m.
WhereJoin Zoom Meeting:
washington.zoom.us…
Meeting ID: 874 857 554
Event typesLectures/Seminars
Event sponsorsClean Energy Institute
Description

The Era of Data-Driven Materials Innovation and Design

Fueled by our abilities to compute materials properties and characteristics orders of magnitude faster than they can be measured and recent advancements in harnessing literature data, we are entering the era of the fourth paradigm of science: data-driven materials design. The Materials Project (www.materialsproject.org) uses supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials and beyond, design novel materials and offer the data for free to the community together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for over 120,000 materials and millions of properties. The resource supports a growing community of data-rich materials research, currently supporting over 130,000 registered users and over 2 million data records served each day through the API. The software infrastructure enables thousands of calculations per week – enabling screening and predictions - for both novel solid as well as molecular species with target properties.  However, truly accelerating materials innovation also requires rapid synthesis, testing and feedback. The ability to devise data-driven methodologies to guide synthesis efforts is needed as well as rapid interrogation and recording of results – including ‘non-successful’ ones. In this talk, I will highlight some of our ongoing work, including efficient harnessing of community data together with our own computational data enabling iteration between ideas, new materials development, synthesis and characterization as enabled by new algorithmic tools and data-driven approaches.

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