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AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

Publication ,  Journal Article
Gossett, E; Toher, C; Oses, C; Isayev, O; Legrain, F; Rose, F; Zurek, E; Carrete, J; Mingo, N; Tropsha, A; Curtarolo, S
Published in: Computational Materials Science
September 1, 2018

Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a plethora of models have been constructed and trained on existing data to predict properties of new systems. These powerful methods allow researchers to target studies only at interesting materials – neglecting the non-synthesizable systems and those without the desired properties – thus reducing the amount of resources spent on expensive computations and/or time-consuming experimental synthesis. However, using these predictive models is not always straightforward. Often, they require a panoply of technical expertise, creating barriers for general users. AFLOW-ML (AFLOW Machine Learning) overcomes the problem by streamlining the use of the machine learning methods developed within the AFLOW consortium. The framework provides an open RESTful API to directly access the continuously updated algorithms, which can be transparently integrated into any workflow to retrieve predictions of electronic, thermal and mechanical properties. These types of interconnected cloud-based applications are envisioned to be capable of further accelerating the adoption of machine learning methods into materials development.

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Published In

Computational Materials Science

DOI

ISSN

0927-0256

Publication Date

September 1, 2018

Volume

152

Start / End Page

134 / 145

Related Subject Headings

  • Materials
  • 5104 Condensed matter physics
  • 4016 Materials engineering
  • 0912 Materials Engineering
  • 0205 Optical Physics
  • 0204 Condensed Matter Physics
 

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Gossett, E., Toher, C., Oses, C., Isayev, O., Legrain, F., Rose, F., … Curtarolo, S. (2018). AFLOW-ML: A RESTful API for machine-learning predictions of materials properties. Computational Materials Science, 152, 134–145. https://doi.org/10.1016/j.commatsci.2018.03.075
Gossett, E., C. Toher, C. Oses, O. Isayev, F. Legrain, F. Rose, E. Zurek, et al. “AFLOW-ML: A RESTful API for machine-learning predictions of materials properties.” Computational Materials Science 152 (September 1, 2018): 134–45. https://doi.org/10.1016/j.commatsci.2018.03.075.
Gossett E, Toher C, Oses C, Isayev O, Legrain F, Rose F, et al. AFLOW-ML: A RESTful API for machine-learning predictions of materials properties. Computational Materials Science. 2018 Sep 1;152:134–45.
Gossett, E., et al. “AFLOW-ML: A RESTful API for machine-learning predictions of materials properties.” Computational Materials Science, vol. 152, Sept. 2018, pp. 134–45. Scopus, doi:10.1016/j.commatsci.2018.03.075.
Gossett E, Toher C, Oses C, Isayev O, Legrain F, Rose F, Zurek E, Carrete J, Mingo N, Tropsha A, Curtarolo S. AFLOW-ML: A RESTful API for machine-learning predictions of materials properties. Computational Materials Science. 2018 Sep 1;152:134–145.
Journal cover image

Published In

Computational Materials Science

DOI

ISSN

0927-0256

Publication Date

September 1, 2018

Volume

152

Start / End Page

134 / 145

Related Subject Headings

  • Materials
  • 5104 Condensed matter physics
  • 4016 Materials engineering
  • 0912 Materials Engineering
  • 0205 Optical Physics
  • 0204 Condensed Matter Physics