Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery
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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- Applied Physics
- 4018 Nanotechnology
- 4016 Materials engineering
- 0913 Mechanical Engineering
- 0912 Materials Engineering
- 0303 Macromolecular and Materials Chemistry
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Applied Physics
- 4018 Nanotechnology
- 4016 Materials engineering
- 0913 Mechanical Engineering
- 0912 Materials Engineering
- 0303 Macromolecular and Materials Chemistry