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Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery

Published online by Cambridge University Press:  10 September 2018

Corey Oses ,
Cormac Toher  and
Stefano Curtarolo
Corey Oses
Affiliation:
Department of Mechanical Engineering and Materials Science, Duke University, USA; corey.oses@duke.edu
Cormac Toher
Affiliation:
Department of Mechanical Engineering and Materials Science, Duke University, USA; cormac.toher@duke.edu
Stefano Curtarolo
Affiliation:
Duke University, USA; stefano@duke.edu
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Abstract

The expansion of programmatically accessible materials data has cultivated opportunities for data-driven approaches. Workflows such as the Automatic Flow Framework for Materials Discovery not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams and properties of disordered materials. In combination with standardized parameter sets, the wealth of data is ideal for training machine-learning algorithms, which have already been employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. These methods promise to revolutionize the path to synthesis, and ultimately transform the practice of traditional materials discovery to one of rational and autonomous materials design.

Keywords

amorphousceramicoxidecrystallographic structureelectronic structure
Type
Data-Centric Science for Materials Innovation
Information
MRS Bulletin , Volume 43 , Issue 9: Data-Centric Science for Materials Innovation , September 2018 , pp. 670 - 675
Copyright
Copyright © Materials Research Society 2018 

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