| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - chemistry |
| - molecular-property-prediction |
| - 3d-molecules |
| - equivariant-gnn |
| - molecular-embeddings |
| --- |
| |
| # MET-models |
|
|
| Checkpoint release for the MET project: |
|
|
| **Integrating equivariant architectures and charge supervision for data-efficient molecular property prediction** |
|
|
| ## Links |
|
|
| - Paper DOI: https://doi.org/10.1039/D5ME00173K |
| - GitHub repository: https://github.com/mint258/MET |
| - Dataset repository: https://huggingface.co/datasets/Mint258/MET-dateset |
| - Intended model repository: https://huggingface.co/Mint258/MET-models |
|
|
| ## Contents |
|
|
| This upload contains the pretrained and fine-tuned checkpoints used in the public MET repository. |
|
|
| ### Pretrained Charge Models |
|
|
| - `pretrained_ckpt/best_model_dim8.pth` |
| - `pretrained_ckpt/best_model_dim16.pth` |
| - `pretrained_ckpt/best_model_dim32.pth` |
| - `pretrained_ckpt/best_model_dim64.pth` |
| - `pretrained_ckpt/best_model_dim128.pth` |
| - `pretrained_ckpt/best_model_dim256.pth` |
|
|
| ### Fine-Tuned QM7 Models |
|
|
| - `fine-tuned_ckpt/qm7/qm7_data100.pth` |
| - `fine-tuned_ckpt/qm7/qm7_data300.pth` |
| - `fine-tuned_ckpt/qm7/qm7_data500.pth` |
| - `fine-tuned_ckpt/qm7/qm7_data800.pth` |
| - `fine-tuned_ckpt/qm7/qm7_data1000.pth` |
|
|
| ### Fine-Tuned QM9 Dipole Models |
|
|
| - `fine-tuned_ckpt/dipole/dipole_data1000.pth` |
| - `fine-tuned_ckpt/dipole/dipole_data5000.pth` |
| - `fine-tuned_ckpt/dipole/dipole_data20000.pth` |
| - `fine-tuned_ckpt/dipole/dipole_data100000.pth` |
|
|
| ## Recommended Local Layout |
|
|
| To keep the existing command examples in the MET GitHub repository unchanged, download these files into the same relative layout: |
|
|
| ```text |
| pretrained_ckpt/ |
| best_model_dim8.pth |
| best_model_dim16.pth |
| best_model_dim32.pth |
| best_model_dim64.pth |
| best_model_dim128.pth |
| best_model_dim256.pth |
| fine-tuned_ckpt/ |
| qm7/ |
| qm7_data100.pth |
| qm7_data300.pth |
| qm7_data500.pth |
| qm7_data800.pth |
| qm7_data1000.pth |
| dipole/ |
| dipole_data1000.pth |
| dipole_data5000.pth |
| dipole_data20000.pth |
| dipole_data100000.pth |
| ``` |
|
|
| ## Checkpoint Notes |
|
|
| - `best_model_dim128.pth` is the main pretrained checkpoint used in the paper-facing examples and regression checks. |
| - The QM7 checkpoints correspond to atomization energy fine-tuning at different low-data subset sizes. |
| - The QM9 dipole checkpoints correspond to dipole-moment fine-tuning at different low-data subset sizes. |
|
|
| ## Usage |
|
|
| Example charge evaluation: |
|
|
| ```bash |
| python pretrain/charge_predict.py \ |
| --checkpoint_path pretrained_ckpt/best_model_dim128.pth \ |
| --test_data_root data/QM9/test_database.lmdb |
| ``` |
|
|
| Example QM7 evaluation: |
|
|
| ```bash |
| python fine-tune/property_predict.py \ |
| --checkpoint fine-tuned_ckpt/qm7/qm7_data300.pth \ |
| --test_data_root data/QM7/test_database |
| ``` |
|
|
| ## Notes |
|
|
| - This folder is prepared for manual upload to a Hugging Face Model repository. |
| - The checkpoints are distributed separately from the GitHub code repository to keep the source repository lightweight. |
|
|
