High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes-Reference-Cited by-同舟云学术

High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes

Published:2021-03-18 Issue:12 Volume:14 Page:4610-4615
ISSN:1998-0124
Container-title:Nano Research
language:en
Short-container-title:Nano Res.

Author:

Ji Zhong-Hai,Zhang Lili,Tang Dai-Ming,Chen Chien-Ming,Nordling Torbjörn E. M.,Zhang Zheng-De,Ren Cui-Lan,Da Bo,Li Xin,Guo Shu-Yu,Liu Chang,Cheng Hui-Ming

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,General Materials Science,Condensed Matter Physics,Atomic and Molecular Physics, and Optics

Link

https://link.springer.com/content/pdf/10.1007/s12274-021-3387-y.pdf

Reference34 articles.

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2. Ding, F.; Rosén, A.; Bolton, K. Dependence of SWNT growth mechanism on temperature and catalyst particle size: Bulk versus surface diffusion. Carbon 2005, 43, 2215–2217.

3. Ding, F.; Bolton, K.; Rosén, A. Molecular dynamics study of SWNT growth on catalyst particles without temperature gradients. Comput. Mater. Sci. 2006, 35, 243–246.

4. Xu, Z. W.; Yan, T. Y.; Ding, F. Atomistic simulation of the growth of defect-free carbon nanotubes. Chem. Sci. 2015, 6, 4704–4711.

5. Agrawal, A.; Choudhary, A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater. 2016, 4, 053208.

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