HZBSolarOptics/MultiLayerThinFilms

OptoLlama Dataset

Details

The original dataset can be found in the OptoGPT publication 📝 and here on HuggingFace.

Key Enhancements

• Inclusion of an absorption feature in the model ➕📈
• Increased the wave length range to 300-2,000nm 💡

Structure

├── materials/
│   ├── Ag.csv
│   ├── Al.csv
│   ├── ...
│   └── ZnSe.csv
├── train/
│   ├── train-0.safetensors
│   ├── train-1.safetensors
│   ├── ...
│   └── train-9.safetensors
├── test/
│   └── test.safetensors
└── tokens.json

Each *.safetensors file contains 1 million thin film structures (indexed by tokens.json) as well as their simulated absorption, reflection and transmission (RAT) spectrum.

The spectrum tensors have the shape (n_samples, 3 [RAT], 171 [bins],) and are stored in float16. The thin film layers have the shape (n_samples, 21 [max_depth incl. EOS and PAD],) and are stored as long.

The thin film layer sequence is ordered from top to bottom, i.e., the lowest index is the top of the thin film layers (touches the air), and the highest index is the bottom layer (touching the back substrate).

Loading Data

This shows an example on how to load (a subset) of the entire data

from safetensors.torch import load_file

data = load_file("train/train-0.safetensors")

spectra = data['spectra']
thin_films = data['thin_films']
print(spectra.shape, thin_films.shape)

>>> torch.Size([1000000, 3, 171]) torch.Size([1000000, 21])

Details

• All spectra have been simulated with tmm_fast
• The n (refractive index), k (extinction coefficients) and wl (wavelength) of all materials can be found in CSV files in the /materials folder
• The training and test data are pre-split and can be found in the /train and /test folder respectively
• A full list of the vocabular, i.e., all possible tokens, can be found in the tokens.json, with the the format of <material>_<thickness_in_nm>
• There are additional tokens for end of sequences (EOS), padding (PAD) and masking (MASK)
• We offer a sub-samples version of the entire test dataset called cropped

Train samples	Test samples	Test samples (cropped)
10,000,000	1,000,000	128,000

Acknowledgements

This work is supported by the Helmholtz Association Initiative and Networking Fund through the Helmholtz AI platform, and the HAICORE@KIT grant.

Citations

If you find our work helpful, please feel free to cite as following:

@article{ma2024optogpt,
  title={OptoGPT: a foundation model for inverse design in optical multilayer thin film structures},
  author={Ma, Taigao and Wang, Haozhu and Guo, L Jay},
  journal={Opto-Electronic Advances},
  volume={7},
  number={7},
  year={2024},
  publisher={Opto-Electronic Advance},
  doi={10.29026/oea.2024.240062}
}

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