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: 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).
General usage
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A python code to perform regression with TensorFlow is avalaible in
models/regression_CNN/tf_fgrid_dnn_validtrain.py
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A jupyter notebook to generate atomic configurations for LAMMPS input file is avalaible in
generate_LAMMPS_input/generate_LAMMPS_configuration_input.ipynb
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A simple jupyter notebook to perform predictions with scikit-learn package is avalaible in
models/simple/simple_machine_learning.ipynb
This package is still under developement. More features (e.g., search algorithm with TensorFlow code) will be added soon.
Authors
Paul Hanakata
Citation
If you use this package/code 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}
}