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

Shulin Tan
1/19/2025
physics.app-phcond-mat.mes-hallcs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Predicting mechanical properties of Carbon Nanotube (CNT) images Using Multi-Layer Synthetic Finite Element Model Simulations

82% match2023

Physics-Informed Neural Network for Predicting Out-of-Training-Range TCAD Solution with Minimized Domain Expertise

81% match2024

Multimodal Machine Learning for Integrating Heterogeneous Analytical Systems

81% match2026

Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie

81% match2026

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling

79% match2024

Insight Gained from Migrating a Machine Learning Model to Intelligence Processing Units

79% match2024

Cite this paper

@article{tan2025dissertation,
  title  = {Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors},
  author = {Shulin Tan},
  year   = {2025},
  eprint = {2501.14813},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.14813v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2501.14813.svg)](https://codeofpaper.com/paper/2501.14813)

Discussion