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James A. Warren to present The Fred Kavli Distinguished Lectureship in Materials Science address

Published online by Cambridge University Press:  10 November 2017

Abstract

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News
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Copyright © Materials Research Society 2017 

James A. Warren, Director of the Materials Genome Program in the Material Measurement Laboratory of the National Institute of Standards and Technology (NIST), will give the talk, “The Materials Genome Initiative and Artificial Intelligence” at the 2017 MRS Fall Meeting in Boston.

After receiving his PhD degree in theoretical physics at the University of California, Santa Barbara, which was preceded by an AB degree (also in physics) from Dartmouth College, Warren took a position as a National Research Council postdoctoral researcher in the Metallurgy Division at NIST. In 1995, with three other junior NIST staff members, he co-founded the NIST Center for Theoretical and Computational Materials Science, which he has directed since 2001. From 2005 to 2013, he was the leader of the Thermodynamics and Kinetics Group. His research has been broadly concerned with developing both models of materials phenomena and the tools to enable the solution of these models. Specific foci over the years have included solidification, pattern formation, grain structures, creep, diffusion, wetting, and spreading in metals. In 2010–2011, Warren was part of the Ad Hoc Committee within the Office of Science and Technology Policy’s National Science and Technology Council (NSTC), which crafted the founding white paper on the administration’s Materials Genome Initiative (MGI). Since 2012, he has served as the executive secretary of the NSTC MGI subcommittee, coordinating an interagency effort to achieve the goals laid out in the MGI.

The US MGI is now more than six years old. With a goal of accelerating the discovery, design, development, and deployment of new materials into manufactured products, the MGI is focused on the creation of a materials innovation infrastructure. NIST has framed its support for the MGI around the need for a data infrastructure that enables the rapid discovery of existing data and models, the tools to assess and improve the quality of those data, and the development of new methods and metrologies based on those data. In partnership with agencies across government, academia, and industry, these approaches are now yielding significant advances.