| | --- |
| | 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. |
| |
|
