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We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery.
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“Integrated simulation of active carbon nanotube forest growth and mechanical compression,”
Matthew R Maschmann, · 2015
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Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He, · 2017
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“Evaluating the forces generated during carbon nanotube forest growth and self-assembly,”
Taher Hajilounezhad, Damola M Ajiboye, and Matthew R Maschmann, · 2019
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Sonia Norouzi-Esfahany, Mehrdad Kokabi, and Ghazaleh Alamdarnejad, · 2020
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“Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning,”
Taher Hajilounezhad, Rina Bao, Kannappan Palaniappan, Filiz Bunyak, Prasad Calyam, and Matthew R. Maschmann, · 2021
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Hossein Kashiani, Shoaib Meraj Sami, Sobhan Soleymani, and Nasser M Nasrabadi, · 2022
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Amirreza Daghighi, Gerardo M. Casanola-Martin, Troy Timmerman, Dejan Milenković, Bono Lučić, and Bakhtiyor Rasulev, · 2022
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“CNT-NeRF: Carbon nanotube forest depth layer decomposition in SEM imagery using generative adversarial networks,”
Nguyen P Nguyen, Ramakrishna Surya, Prasad Calyam, Kannappan Palaniappan, Matthew Maschmann, and Filiz Bunyak, · 2023
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