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| license: apache-2.0 |
| tags: |
| - deep-learning |
| - chemistry |
| - computational-chemistry |
| - vibrational-spectroscopy |
| - molecular-structure |
| --- |
| [](https://arxiv.org/abs/2503.07014) |
| [](https://huggingface.co/xinyulu/vib2mol) |
| [](https://huggingface.co/datasets/xinyulu/vibench) |
| [](https://doi.org/10.6084/m9.figshare.28579832) |
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| # Model Overview |
| Vib2Mol is a versatile deep learning model designed for two key tasks in computational chemistry: **spectrum-structure retrieval** and ***de novo* spectrum-to-structure generation**. It leverages a novel architecture to build a robust relationship between molecular structures and their corresponding vibrational spectra (specifically, Infrared and Raman). |
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| This model is a core component of the research presented in our paper, ***Vib2Mol: from vibrational spectra to molecular structures—a versatile deep learning model***. More details can be found in our [arXiv paper](https://arxiv.org/abs/2503.07014) and [GitHub repository](https://github.com/X1nyuLu/vib2mol). |
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| # How to Use |
| To use this model for inference, you can load the model checkpoint and use the `infer_retrieval.sh` or `infer_generation.sh` scripts from our GitHub repository. |
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| # Acknowledgements |
| This work was supported by the National Natural Science Foundation (Grant No: 22227802, 22021001, 22474117 and 22272139) of China and the Fundamental Research Funds for the Central Universities (20720220009 and 20720250005) and Shanghai Innovation Institute. |
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| # Contact |
| Welcome to contact us or raise issues if you have any questions. |
| Email: xinyulu@stu.xmu.edu.cn |
