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