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:

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:

python pretrain/charge_predict.py \
  --checkpoint_path pretrained_ckpt/best_model_dim128.pth \
  --test_data_root data/QM9/test_database.lmdb

Example QM7 evaluation:

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.