AFLOW-ML: A RESTful API for machine-learning predictions of materials properties-Reference-Cited by-同舟云学术

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

Published:2018-09 Issue: Volume:152 Page:134-145
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Gossett Eric,Toher Cormac,Oses Corey,Isayev Olexandr,Legrain Fleur,Rose Frisco,Zurek Eva,Carrete Jesús,Mingo Natalio,Tropsha Alexander,Curtarolo Stefano^ORCID

Funder

DOD-ONR

NSF

Duke University—Center for Materials Genomics

National Science Foundation Graduate Research Fellowship

Eshelman Institute for Innovation Award

Extreme Science and Engineering Discovery Environment

National Science Foundation

m-era.net

ANR Carnot “MAPPE”

Alexander von Humboldt Foundation

Max Planck Society

Publisher

Elsevier BV

Subject

Computational Mathematics,General Physics and Astronomy,Mechanics of Materials,General Materials Science,General Chemistry,General Computer Science

Reference71 articles.

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2. Modeling off-stoichiometry materials with a high-throughput ab-initio, approach;Yang;Chem. Mater.,2016

3. The AFLOW standard for high-throughput materials science calculations;Calderon;Comput. Mater. Sci.,2015

4. Ordered structures in Rhenium binary alloys from first-principles calculations;Levy;J. Am. Chem. Soc.,2011

5. Structure maps for hcp metals from first-principles calculations;Levy;Phys. Rev. B,2010

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