Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors
This work addresses performance prediction and design optimization for CNTFETs in materials science, but appears incremental as it applies existing methods to a specific domain without claiming major breakthroughs.
The thesis applied machine learning techniques like neural networks and generative flow networks to predict the performance of carbon nanotube field-effect transistors (CNTFETs), probe conductivity properties, and generate processing information for target performance, but did not report specific numerical results.
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.