Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

Taher Hajilounezhad; Rina Bao; Kannappan Palaniappan; Filiz Bunyak; Prasad Calyam; Matthew R. Maschmann

doi:10.1038/s41524-021-00603-8

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

Taher Hajilounezhad
, Rina Bao
, Kannappan Palaniappan
, Filiz Bunyak
, Prasad Calyam
, Matthew R. Maschmann

University of Missouri-Columbia

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	134
Journal	npj Computational Materials
Volume	7
Issue number	1
DOIs	https://doi.org/10.1038/s41524-021-00603-8
State	Published - Dec 2021
Externally published	Yes

ASJC Scopus Subject Areas

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

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10.1038/s41524-021-00603-8

Cite this

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title = "Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning",

author = "Taher Hajilounezhad and Rina Bao and Kannappan Palaniappan and Filiz Bunyak and Prasad Calyam and Maschmann, \{Matthew R.\}",

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year = "2021",

month = dec,

doi = "10.1038/s41524-021-00603-8",

language = "English",

volume = "7",

journal = "npj Computational Materials",

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T1 - Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

AU - Hajilounezhad, Taher

AU - Bao, Rina

AU - Palaniappan, Kannappan

AU - Bunyak, Filiz

AU - Calyam, Prasad

AU - Maschmann, Matthew R.

PY - 2021/12

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UR - https://www.scopus.com/pages/publications/85113205361#tab=citedBy

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DO - 10.1038/s41524-021-00603-8

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SN - 2057-3960

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JO - npj Computational Materials

JF - npj Computational Materials

IS - 1

M1 - 134

ER -

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

ASJC Scopus Subject Areas

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