Skip to content

zhantaochen/panoramic-phonon-ued

Repository files navigation

Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning

This is the code repository for the paper "Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning" (https://arxiv.org/pdf/2202.06199.pdf). Please direct any questions about codes to Zhantao ([email protected]).

Key python files

adjoint_bte/torchdiffeq

  • Modified from torchdiffeq of the commit id 4678a03aaeec9ad7daa9888d4e7988d17d011199 https://github.com/rtqichen/torchdiffeq/tree/4678a03aaeec9ad7daa9888d4e7988d17d011199. The major modifications are mainly made inside adjoint.py and solvers.py, which reuse the calculated phonon energy distribution function in the backward pass. This will significantly increase memory usage but provide better numerical stability during the backward pass.

adjoint_bte/phonon_bte.py

  • defines the forward model of phonon Boltzmann transport equation for a specified layer and material

adjoint_bte/model_heterostructure.py

  • assemble two layers into a heterostructure with specified boundary and interface conditions

To reproduce results presented in the paper, for synthetic data demonstrations:

  • generate synthetic data with the file Simulation_HeteroStruct.py, where you can determine several components like materials and interface transmittances (the main target to be learned) and simulate time-dependent atomic mean-squared displacements (MSD) of each layer;
  • learn phonon transport properties (i.e., transmittance) with the file Learn_SyntheticData.py.

The results corresponds to real UED experiment can be reproduced by directly running Learn_ExpData.py.

Please note that we used relatively large batch size (bs_params) that utilized roughly 20GB of memories. If you have a GPU with smaller memory size, you may want to reduce the batch size accordingly in order to perform the learning.

Citing

@article{chen2022panoramic,
  title={Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning},
  author={Chen, Zhantao and Shen, Xiaozhe and Andrejevic, Nina and Liu, Tongtong and Luo, Duan and Nguyen, Thanh and Drucker, Nathan C and Kozina, Michael E and Song, Qichen and Hua, Chengyun and others},
  journal={arXiv preprint arXiv:2202.06199},
  year={2022}
}

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages