Python package to model and to perform topology optimization for graphene kirigami using deep learning. We use convolutional neural networks (similar to VGGNet architecure) for regression.
- Python script to create and test fine grid dataset from coarse grid dataset is avalaible in
bin\create-and-test-fine-grid.py
- Regression and optimization
- A python code to perform regression with TensorFlow is avalaible in
notebooks\CNN\01-regression.ipynb
- A python code to perform search optimal design with TensorFlow is avalaible in
notebooks\CNN\02-search-designs.ipynb
- Dataset
- Raw dataset of coarse-grained configs is avaialble in
raw
. This dataset generated using AIREBO potential with 1.7 mincutoff which is the default of CH.airebo.
This package is still under developement. More features will be added soon.
All dependencies are included in environment.yml
and requirements.txt
file. The enviroment can be generated by using
mamba env create --prefix ./env --file environment.yml --force
or
conda env create --prefix ./env --file environment.yml --force
If mamba do not exists then install it via
conda activate base
conda install mamba -n base -c conda-forge
If you add (remove) dependencies to (from) the environment.yml file or the requirements.txt file after the environment has already been created, then you can re-create the environment with the following command.
conda activate base
mamba env update --prefix ./env --file environment.yml --prune