metadata
license: cc-by-4.0
datasets:
- HZBSolarOptics/MultiLayerThinFilms
metrics:
- mae
tags:
- science
- material
- inverse
- design
OptoGPT++
Meet OptoGPT++ — an enhanced implementation of the OptoGPT, a decoder-only transformer that aims to solve inverse design of multi-layer thin film structures.
Key Enhancements
- Inclusion of an absorption feature in the model ➕📈
- Increased the maximum wave length to 2,000nm 💡
- Longer training time for better predictive performance 🤯
Supporting Material
OptoGPT++: https://github.com/jnitzz/OptoLlama
OptoGPT: https://github.com/taigaoma1997/optogpt
ArXiV: 📝 https://arxiv.org/abs/2304.10294
Usage
Install Dependencies
python -m pip install torch
python -m pip install safetensors
Load Model Checkpoint
from safetensors.torch import load_file
model = OptoGPT()
safetensors_path = "optogpt-model.safetensors"
state_dict = load_file(safetensors_path)
model.load_state_dict(state_dict)
Useful Information
| Stat | Value |
|---|---|
| #Parameters | 108,381,113 |
| Best validation MAE | 0.0408 |
| Epochs trained | 1,000 |
| Best epoch. | 996 |
| Batch size | 256 |
| n_blocks | 6 |
| n_heads | 8 |
| d_model | 1,024 |
| max_seq_length | 20 |
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}
}
