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@@ -21,11 +21,13 @@ Welcome to IBM's series of large foundation models for sustainable materials. Ou
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GitHub: [GitHub Link](https://github.com/IBM/materials/tree/main)
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Paper: [arXiv:2407.20267](https://arxiv.org/abs/2407.20267)
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# SMILES-based Transformer Encoder-Decoder (SMI-TED)
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Paper (pre-print): [arXiv:2407.20267](https://arxiv.org/abs/2407.20267)
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Paper: [Communications Chemistry](https://rdcu.be/eujAk)
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# SMILES-based Transformer Encoder-Decoder (SMI-TED)
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This repository provides PyTorch source code associated with our publication, "A Large Encoder-Decoder Family of Foundation Models for Chemical Language".
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## Citations
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```
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@article{soares2025open,
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title = {An open-source family of large encoder-decoder foundation models for chemistry},
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author = {Soares, Eduardo and Vital Brazil, Emilio and Shirasuna, Victor and Zubarev, Dmitry and Cerqueira, Renato and Schmidt, Kristin},
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journal = {Communications Chemistry},
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volume = {8},
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pages = {193},
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year = {2025},
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publisher = {Nature Portfolio},
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doi = {10.1038/s42004-025-01585-0},
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url = {https://doi.org/10.1038/s42004-025-01585-0}
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}
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```
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```
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@misc{soares2024largeencoderdecoderfamilyfoundation,
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title={A Large Encoder-Decoder Family of Foundation Models For Chemical Language},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2407.20267},
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}
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```
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