ML_for_kirigami_design

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.

Paper

See our published paper:

1. P. Z. Hanakata, E. D. Cubuk, D. K. Campbell, H.S. Park, Accelerated search and design of stretchable graphene kirigami using machine learning, Phys. Rev. Lett, 121, 255304 (2018).
2. P. Z. Hanakata, E. D. Cubuk, D. K. Campbell, H.S. Park, Forward and inverse design of kirigami via supervised autoencoder, Phys. Rev. Research, 121, 255304 (2018).

General usage

1. Data generation and data handling

• A jupyter notebook to generate atomic configurations for LAMMPS input file is avalaible in generate_LAMMPS_input/generate_LAMMPS_configuration_input.ipynb. New methods to generate parallel cuts are now avalaible.
• A simple jupyter notebook to convert coarse-grained dataset to fine-grid dataset is avalaible in models/regression_CNN/convert_coarse_to_fine.ipynb

2. Regression and optimization

• A python code to perform regression with TensorFlow is avalaible in models/regression_CNN/tf_fgrid_dnn_validtrain.py
• A python code to perform search optimal design with TensorFlow is avalaible in models/regression_CNN/tf_cnn_search_large_v2.py
• A simple jupyter notebook to perform predictions with scikit-learn package is avalaible in models/simple/simple_machine_learning.ipynb

4. Dataset

• Raw dataset of coarse-grained grid can be found in mddata. This dataset generated using AIREBO potential with 1.7 mincutoff which is the default of CH.airebo.

3. Supervised Autoencoder

• A sAE notebook to perform forward and inverse design is now avaliable in models_supervisedAutoencoder_forwardInverseDesign/supervisedAE_for_kirigamiDesign.ipynb. See notebook for details of the code.

This package is still under developement. More features will be added soon.

To download

git clone https://github.com/phanakata/ML_for_kirigami_design.git

Authors

Paul Hanakata

Citation

If you use this package/code/dataset, build on or find our research is useful for your work please cite

@article{hanakata-PhysRevLett.121.255304,
  title = {Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning},
  author = {Hanakata, Paul Z. and Cubuk, Ekin D. and Campbell, David K. and Park, Harold S.},
  journal = {Phys. Rev. Lett.},
  volume = {121},
  issue = {25},
  pages = {255304},
  numpages = {6},
  year = {2018},
  month = {Dec},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.121.255304},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.121.255304}
}

@article{PhysRevResearch.2.042006,
  title = {Forward and inverse design of kirigami via supervised autoencoder},
  author = {Hanakata, Paul Z. and Cubuk, Ekin D. and Campbell, David K. and Park, Harold S.},
  journal = {Phys. Rev. Research},
  volume = {2},
  issue = {4},
  pages = {042006},
  numpages = {6},
  year = {2020},
  month = {Oct},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevResearch.2.042006},
  url = {https://link.aps.org/doi/10.1103/PhysRevResearch.2.042006}
}

References

• [1] Paul Z. Hanakata, E. D. Cubuk, D. K. Campbell, H.S. Park, Phys. Rev. Research, 2, 042006(R) (2020).
• [2] Paul Z. Hanakata, E. D. Cubuk, D. K. Campbell, H.S. Park, Phys. Rev. Lett, 121, 255304 (2018).