| --- |
| license: apache-2.0 |
| library_name: pytorch |
| tags: |
| - materials-science |
| - crystal-structures |
| - solid-state-batteries |
| - representation-learning |
| - screening |
| model-index: |
| - name: SSB Screening Model (RTX6000x2) |
| results: |
| - task: |
| type: text-classification |
| name: Screening Proxy (3-class) |
| metrics: |
| - type: accuracy |
| value: 0.8118937 |
| - type: f1 |
| value: 0.8060277 |
| - type: precision |
| value: 0.7671543 |
| - type: recall |
| value: 0.8694215 |
| - type: val_loss |
| value: 0.2856999 |
| --- |
| |
| # SSB Screening Model (RTX6000x2) |
|
|
| ## Model Summary |
| This model is a lightweight MLP classifier trained on NPZ-encoded inorganic crystal structure features for solid-state battery (SSB) screening proxies. It is intended to prioritize candidate structures, not to replace DFT or experimental validation. |
|
|
| - **Architecture**: MLP (input_dim=144, hidden_dims=[512, 256, 128], dropout variable by sweep) |
| - **Output**: 3-class classification proxy for screening tasks |
| - **Training Regime**: supervised training on curated NPZ dataset with class-weighted loss |
| - **Best checkpoint**: `checkpoint_epoch45.pt` (lowest observed val_loss in logs) |
| |
| ## Intended Use |
| - **Primary**: ranking/prioritization of SSB electrolyte candidates |
| - **Not intended**: absolute property prediction or experimental ground truth replacement |
| |
| ## Training Data |
| - **Dataset**: `ssb_npz_v1` (curated NPZ features) |
| - **Split**: 80/10/10 (train/val/test) |
| - **Features**: composition + lattice + derived scalar statistics (144-dim) |
| |
| ## Evaluation |
| Metrics from the latest run summary: |
| - **Val loss**: 0.2857 |
| - **Val accuracy**: 0.8119 |
| - **Holdout accuracy**: 0.8096 |
| - **F1**: 0.8060 |
| - **Precision**: 0.7672 |
| - **Recall**: 0.8694 |
| |
| ## Limitations |
| - The model is a proxy classifier; it does not predict ground-truth physical properties. |
| - Performance is tied to the training distribution of `ssb_npz_v1`. |
| - Chemical regimes underrepresented in the training set may be poorly ranked. |
| |
| ## Training Configuration (abridged) |
| - Optimizer: AdamW |
| - LR: sweep (best around ~3e-4) |
| - Weight decay: sweep (0.005–0.02) |
| - Scheduler: cosine |
| - Batch size: sweep (128–512) |
| - Epochs: sweep (20–60) |
| - Gradient accumulation: sweep (1–4) |
| |
| ## Citation |
| If you use this model, please cite the dataset and training pipeline from the Nexa_compute repository. |
|
|
