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README.md

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+---
+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.