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

Publication ,  Journal Article
Oses, C; Toher, C; Curtarolo, S
Published in: MRS Bulletin
September 1, 2018

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.

Duke Scholars

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Published In

MRS Bulletin

DOI

ISSN

0883-7694

Publication Date

September 1, 2018

Volume

43

Issue

9

Start / End Page

670 / 675

Related Subject Headings

  • Applied Physics
  • 4018 Nanotechnology
  • 4016 Materials engineering
  • 0913 Mechanical Engineering
  • 0912 Materials Engineering
  • 0303 Macromolecular and Materials Chemistry
 

Citation

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Oses, C., Toher, C., & Curtarolo, S. (2018). Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery. MRS Bulletin, 43(9), 670–675. https://doi.org/10.1557/mrs.2018.207
Oses, C., C. Toher, and S. Curtarolo. “Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery.” MRS Bulletin 43, no. 9 (September 1, 2018): 670–75. https://doi.org/10.1557/mrs.2018.207.
Oses C, Toher C, Curtarolo S. Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery. MRS Bulletin. 2018 Sep 1;43(9):670–5.
Oses, C., et al. “Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery.” MRS Bulletin, vol. 43, no. 9, Sept. 2018, pp. 670–75. Scopus, doi:10.1557/mrs.2018.207.
Oses C, Toher C, Curtarolo S. Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery. MRS Bulletin. 2018 Sep 1;43(9):670–675.
Journal cover image

Published In

MRS Bulletin

DOI

ISSN

0883-7694

Publication Date

September 1, 2018

Volume

43

Issue

9

Start / End Page

670 / 675

Related Subject Headings

  • Applied Physics
  • 4018 Nanotechnology
  • 4016 Materials engineering
  • 0913 Mechanical Engineering
  • 0912 Materials Engineering
  • 0303 Macromolecular and Materials Chemistry