README.md

metadata

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.