Papers

Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors

S Tan - arXiv preprint arXiv:2501.14813, 2025 - arxiv.org
Physics paper physics.app-ph Suggest

… aligned CNT networks and used simulation-based inference to extract key parameters … We show that simulation-based inference can be a powerful tool for building models …

Link to paper

BibTeX

@article{2501.14813v1,
Author        = {Shulin Tan},
Title         = {Dissertation Machine Learning in Materials Science -- A case study in
  Carbon Nanotube field effect transistors},
Eprint        = {2501.14813v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {physics.app-ph},
Abstract      = {In this thesis, I explored the use of several machine learning techniques,
including neural networks, simulation-based inference, and generative flow
networks, on predicting CNTFETs performance, probing the conductivity
properties of CNT network, and generating CNTFETs processing information for
target performance.},
Year          = {2025},
Month         = {Jan},
Url           = {http://arxiv.org/abs/2501.14813v1},
File          = {2501.14813v1.pdf}
}

Share