Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review-Reference-Cited by-同舟云学术

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Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review

Published:2022-01 Issue: Volume:201 Page:110939
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Vivanco-Benavides Luis Enrique,Martínez-González Claudia Lizbeth,Mercado-Zúñiga Cecilia,Torres-Torres Carlos

Funder

Instituto Politécnico Nacional

Consejo Nacional de Ciencia y Tecnología

Publisher

Elsevier BV

Subject

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

Reference177 articles.

1. Materials informatics;Ramakrishna;J. Intell. Manuf.,2019

2. Materials Informatics: The Materials “Gene” and Big Data;Rajan;Annu. Rev. Mater. Res.,2015

3. Artificial neural network approach for atomic coordinate prediction of carbon nanotubes;Acı;Appl. Phys. A Mater. Sci. Process.,2016

4. Artificial neural network for predictive synthesis of single-walled carbon nanotubes by aerosol CVD method;Iakovlev;Carbon N. Y.,2019

5. M.F.L. De Volder, S.H. Tawfick, R.H. Baughman, A.J. Hart, Carbon nanotubes: Present and future commercial applications, Science (80-.). 339 (2013) 535–539. https://doi.org/10.1126/science.1222453.

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