metadata
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
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.