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The Materials Genome Initiative and artificial intelligence

Published online by Cambridge University Press:  11 June 2018

James A. Warren
James A. Warren*
Affiliation:
Materials Genome Program, National Institute of Standards and Technology, USA; [email protected]
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Abstract

The Materials Genome Initiative (MGI) seeks to accelerate the discovery, design, development, and deployment of new materials through the creation of a materials innovation infrastructure. This infrastructure is essentially a system for providing data and tools that encapsulate our existing knowledge about materials, and the means to create new knowledge. Given this approach, MGI is also deeply linked to the ongoing exponential growth in applications of machine learning and artificial intelligence (AI) to materials research. This article explores the connections between MGI, the consequent need for data publication, the implications for data-driven science, and the application of AI to materials design. Examples will demonstrate how materials research is transforming in remarkable ways, and that the MGI vision of accelerated materials discovery is within reach.

Keywords

government interactionssimulationstructuralfunctional
Type
Technical Feature
Information
MRS Bulletin , Volume 43 , Issue 6: Materials for Nonreciprocal Photonics , June 2018 , pp. 452 - 457
Copyright
Copyright © Materials Research Society 2018 

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Footnotes

The following article is based on The Fred Kavli Distinguished Lectureship in Materials Science given by James A. Warren at the 2017 MRS Fall Meeting Plenary Session in Boston, Mass.

References

https://mgi.nist.gov.Google Scholar
https://www.mgi.gov.Google Scholar
Lass, E., Stoudt, M., Campbell, C.E. (forthcoming).Google Scholar
Olson, G., Science 277 (5330), 1237 (1997).CrossRefGoogle Scholar
National Research Council, “Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security” (National Academies Press, Washington, DC, 2008).Google Scholar
https://materialsproject.org.Google Scholar
The Minerals, Metals & Materials Society (TMS), “Building a Materials Data Infrastructure: Opening New Pathways to Discovery and Innovation in Science and Engineering” (TMS, Pittsburgh, 2017).Google Scholar
Ward, C.H., Warren, J.A., Hanisch, R.J., Integr. Mater. Manuf. Innov. 3 (22), (2014).CrossRefGoogle Scholar
https://mgi.nist.gov/materials-data-curation-system.Google Scholar
https://mgi.nist.gov/materials-resource-registry.Google Scholar
Botu, V., Batra, R., Chapman, J., Ramprasad, R., J. Phys. Chem. C 121 (1), 511 (2017).CrossRefGoogle Scholar
https://materialsdata.nist.gov.Google Scholar
Abbott, B.P. et al., Phys. Rev. Lett. 116, 061102 (2016).CrossRefGoogle Scholar
DeCost, B.L., Jain, H., Rollett, A.D., Holm, E.A., JOM 69, 456 (2016).CrossRefGoogle Scholar
Hattrick-Simpers, J.R., Gregoire, J.M., Kusne, A.G., APL Mater. 4, 053211 (2016).CrossRefGoogle Scholar
Xue, D., Balachandran, P.V., Hogden, J., Theiler, J., Xue, D., Lookman, T., Nat. Commun. 7, 11241 (2016).CrossRefGoogle Scholar
Gershenfeld, N., Gershenfeld, A., Cutcher-Gershenfeld, J., Designing Reality (Basic Books, New York, 2017).Google Scholar