| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - chemistry |
| - molecular-representation |
| - density-matrix |
| - equivariant |
| --- |
| |
| # DeMiR: Density-Matrix-Informed Representations for Molecular Property Prediction |
|
|
| Pretrained weights for DeMiR, a molecular representation model pretrained by |
| predicting DFT electron density matrices. Code: https://github.com/dhyeonk-0008/DeMiR |
|
|
| ## Files |
|
|
| | File | Description | SHA-256 | |
| | --- | --- | --- | |
| | `demir.pth` | PyTorch `state_dict` (6.4M parameters, 25.8 MB) | `dbaefe607b31c4cccd6e0ea0c63eef40191c8903f055ee12eb89c614a88ed815` | |
|
|
| This is the checkpoint used for all results in the paper (validation-loss minimum |
| of the joint QM9 + QMugs training run). |
|
|
| ## Usage |
|
|
| ```bash |
| git clone https://github.com/dhyeonk-0008/DeMiR && cd DeMiR |
| # install as in the repository README, then: |
| hf download DeonX/DeMiR demir.pth --local-dir checkpoints |
| |
| python scripts/inference/predict_density.py \ |
| --smiles "CCO" \ |
| --checkpoint checkpoints/demir.pth \ |
| --output-npz ethanol.npz \ |
| --device cpu |
| ``` |
|
|
| The architecture is inferred from the weights. See the repository README for |
| embedding extraction and property-prediction probes. |
|
|
| ## Training data |
|
|
| B3LYP/cc-pVDZ density matrices computed with PySCF for QM9 molecules and a |
| representative subset of QMugs (adding S, P, Cl, Br and larger drug-like |
| molecules). Median real-space density NMAE on held-out molecules: 1.00% (QM9), |
| 2.16% (QMugs). |
|
|
| ## Limitations |
|
|
| - Neutral, closed-shell molecules in the cc-pVDZ basis only (H, C, N, O, F, P, S, Cl, Br). |
| - Bromine-containing molecules reconstruct poorly (NMAE > 8%). |
| - The eSCN backbone samples per-edge frames, so repeated forward passes differ |
| slightly (up to ~1e-2 in individual density-matrix elements) unless the random seed is fixed. |
|
|
| ## License |
|
|
| MIT. The QM9 and QMugs source datasets keep their own licenses. |
|
|
