Master's thesis project - Condensed matter physics problems and deep learning solutions
The main idea lies in leveraging deep learning techniques to recognize the shift angles between layers of Van der Waals heterostructures. In our specific case, the material in question is Nanoporous Graphene (NPG). Scanning Tunneling Microscopy (STM) is employed for imaging the graphene layers, thus, enabling the detection of twisted bilayer and multilayer domains. On the simulation side, an analytic model is adopted to shed light on the number of misoriented layers, and their weak interaction: for each layer we can write an accurate analytical form that can describe the in-plane density.
The first thing to do is to build a dataset that physically represents the problem: The goal is to recognize how much two or more layers of a graphene-derived material are twisted relative to each other, as it is known that there are certain 'magic' angles at which particular properties are exhibited. The first step is to produce as many STM image simulations as possible and collect them in a dataset that links them to the twist angles for each layer
- Python, jupyter notebook (ipynb)
- How/where to download your program
- Any modifications needed to be made to files/folders
Contributors names and contact info
MSc Giuseppina Pia Varano @GiusyVarano
- 0.2
- See commit change or See release history
- 0.1
- Initial Release
This project is licensed under the [NAME HERE] License - see the LICENSE.md file for details
Inspiration, code snippets, etc.