Machine-learned multi-system surrogate models for materials prediction-Reference-Cited by-同舟云学术

Machine-learned multi-system surrogate models for materials prediction

Published:2019-04-18 Issue:1 Volume:5 Page:
ISSN:2057-3960
Container-title:npj Computational Materials
language:en
Short-container-title:npj Comput Mater

Author:

Nyshadham Chandramouli,Rupp Matthias^ORCID,Bekker Brayden,Shapeev Alexander V.,Mueller Tim^ORCID,Rosenbrock Conrad W.,Csányi Gábor,Wingate David W.,Hart Gus L. W.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation

Link

http://www.nature.com/articles/s41524-019-0189-9.pdf

Reference49 articles.

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3. Jain, A. et al. Commentary: The materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).

4. C. Draxl and M. Scheffler NOMAD: the FAIR concept for big-data-driven materials science. MRS Bull. 43, 676–682 (2018).

5. Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).

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