SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates-Reference-Cited by-同舟云学术

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SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

Published:2018-08-07 Issue:8 Volume:2 Page:
ISSN:2475-9953
Container-title:Physical Review Materials
language:en
Short-container-title:Phys. Rev. Materials

Author:

Ouyang Runhai,Curtarolo Stefano,Ahmetcik Emre,Scheffler Matthias,Ghiringhelli Luca M.

Funder

Horizon 2020

U.S. Department of Defense

Office of Naval Research

Alexander von Humboldt-Stiftung

Publisher

American Physical Society (APS)

Subject

Physics and Astronomy (miscellaneous),General Materials Science

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevMaterials.2.083802/fulltext

Reference57 articles.

1. A high-throughput infrastructure for density functional theory calculations

2. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

3. The Computational Materials Repository

4. Big data are shaping the future of materials science

5. Big Data of Materials Science: Critical Role of the Descriptor

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