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Strategies for accelerating the adoption of materials informatics

Published online by Cambridge University Press:  10 September 2018

Logan Ward ,
Muratahan Aykol ,
Ben Blaiszik ,
Ian Foster ,
Bryce Meredig ,
James Saal  and
Santosh Suram
Logan Ward
Affiliation:
The University of Chicago, and Data Science and Learning Division, Argonne National Laboratory, USA; [email protected]
Muratahan Aykol
Affiliation:
Toyota Research Institute, USA; [email protected]
Ben Blaiszik
Affiliation:
The University of Chicago, and Data Science and Learning Division, Argonne National Laboratory, USA; [email protected]
Ian Foster
Affiliation:
Department of Computer Science, The University of Chicago, and Data Science and Learning Division, Argonne National Laboratory, USA; [email protected]
Bryce Meredig
Affiliation:
Citrine Informatics, USA; [email protected]
James Saal
Affiliation:
QuesTek InnovationsLLC, USA; [email protected]
Santosh Suram
Affiliation:
Toyota Research Institute, USA; [email protected]
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Abstract

Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials R&D methods offer few paths to achieve both outcomes simultaneously. Materials informatics, while a nascent field, offers such a promise through screening, growing databases of materials for new applications, learning new relationships from existing data resources, and building fast predictive models. We highlight key materials informatics successes from the atomic-scale modeling community, and discuss the ecosystem of open data, software, services, and infrastructure that have led to broad adoption of materials informatics approaches. We then examine emerging opportunities for informatics in materials science and describe an ideal data ecosystem capable of supporting similar widespread adoption of materials informatics, which we believe will enable the faster design of materials.

Keywords

combinatorial synthesiscrystallographic structuresimulation
Type
Data-Centric Science for Materials Innovation
Information
MRS Bulletin , Volume 43 , Issue 9: Data-Centric Science for Materials Innovation , September 2018 , pp. 683 - 689
Copyright
Copyright © Materials Research Society 2018 

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