MET-models

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

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

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

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.