Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets-Reference-Cited by-同舟云学术

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Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

Published:2018-08 Issue: Volume:151 Page:278-287
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Yang Zijiang,Yabansu Yuksel C.,Al-Bahrani Reda,Liao Wei-keng,Choudhary Alok N.,Kalidindi Surya R.,Agrawal Ankit

Funder

Air Force Office of Scientific Research

National Science Foundation

U.S. Department of Energy

National Institute of Standards and Technology

Publisher

Elsevier BV

Subject

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

Reference78 articles.

1. Perspective: materials informatics and big data: realization of the fourth paradigm of science in materials science;Agrawal;APL Mater.,2016

2. Vision for data and informatics in the future materials innovation ecosystem;Kalidindi;JOM,2016

3. Key computational modeling issues in integrated computational materials engineering;Panchal;Comput. Aided Des.,2013

4. Computational design of hierarchically structured materials;Olson;Science,1997

5. Automated, high throughput exploration of process–structure–property relationships using the mapreduce paradigm;Wodo;Mater. Discovery,2015

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