Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910801752752128 |
|---|---|
| author | Tan, Shulin |
| author_facet | Tan, Shulin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors Tan, Shulin Applied Physics Mesoscale and Nanoscale Physics Machine Learning Data Analysis, Statistics and Probability 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. |
| title | Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors |
| topic | Applied Physics Mesoscale and Nanoscale Physics Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2501.14813 |