Publications

@misc{Nordling2021CTAM,

title = {Use of AI and machine learning for efficient growth of high-quality single-wall carbon nanotubes},

author = {Torbjörn E. M. Nordling and Shemon Baptiste},

url = {https://ctam2021.conf.tw/},

year  = {2021},

date = {2021-11-19},

booktitle = {45th Conference on Theoretical and Applied Mechanics (CTAM 2021)},

pages = {0},

publisher = {National Taiwan University, Taipei, Taiwan},

address = {Xinhai Road Section 188, Taipei, Taiwan},

abstract = {Optimisation of the growth conditions for manufacturing of single-wall carbon nanotubes (SWCNTs) was challenging until recent demonstration of “High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes” by Ji et al. 2021 in Nano Research (DOI:10.1007/s12274-021-3387-y). The high-throughput screening of growth conditions was conducted by depositing patterned cobalt (Co) nanoparticles on a marked silicon wafer catalysts and varying the temperature, reduction time, carbon precursor, and growth time during chemical vapor deposition. The quality (crystallinity) of the SWCNTs was characterised by the G/D peak intensity (IG/ID) measured by Raman spectroscopy. 1664 samples were used to train and validate machine learning models for prediction of the quality resulting from a particular combination of growth parameters. Here, we expand the work and train an artificial neural network with improved prediction accuracy. The quality depends on the growth parameters in a non-linear fashion with multiple local optima, explaining why it is so hard to optimise the growth conditions without machine learning. With AI growth conditions for high-quality SWCNTs were identified.},

howpublished = {45th Conference on Theoretical and Applied Mechanics (CTAM 2021)},

keywords = {single-wall carbon nanotube; high throughput; machine learning; artificial intelligence; optimization; chemical vapor deposition},

pubstate = {published},

tppubtype = {misc}

}

@misc{Nordling2021ISNST,

title = {Deep learning for optimisation of growth of high-quality single-wall carbon nanotubes},

author = {Torbjörn E. M. Nordling},

url = {https://csie.stust.edu.tw/sysid/csie/ISNST2021/},

year  = {2021},

date = {2021-11-18},

booktitle = {2021 International Symposium on Novel and Sustainable Technology (2021 ISNST)},

pages = {0},

publisher = {Southern Taiwan University of Science and Technology (STUST), Tainan City, Taiwan},

address = {No.1, Nantai St., Yongkang Dist., Tainan City, Taiwan (R.O.C.)},

abstract = {Manufacturing of single-wall carbon nanotubes (SWCNTs) is challenging, but recently we demonstrated optimisation of the growth conditions in “High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes”, published in Nano Research (DOI:10.1007/s12274-021-3387-y). Our high-throughput screening of growth conditions was conducted by depositing patterned cobalt nanoparticles on a marked silicon wafer catalysts and varying the carbon precursor, growth time, reduction time, and temperature during chemical vapor deposition. We characterised the crystallinity of the SWCNTs by the G/D peak intensity (IG/ID) measured by Raman spectroscopy. We trained and validated machine learning models for prediction of the quality resulting from each combination of growth parameters using 1664 samples. This talk focus on the training of an artificial neural network with improved prediction accuracy. The cost function contain multiple local optima, which make it hard to optimise the growth conditions without machine learning.},

howpublished = {2021 International Symposium on Novel and Sustainable Technology (2021 ISNST)},

keywords = {single-wall carbon nanotube; high throughput; machine learning; artificial intelligence; optimization; chemical vapor deposition},

pubstate = {published},

tppubtype = {misc}

}