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README.md

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+---
+    library_name: flax
+    tags:
+    - chemistry
+    - ionic-conductivity
+    - polymer-electrolytes
+    - jax
+    pipeline_tag: tabular-regression
+    ---
+
+    # Arrhenius Predictor for Polymer Electrolytes
+    Paper: https://pubs.acs.org/doi/10.1021/acscentsci.2c01123
+    This model predicts ionic conductivity ($\ln \sigma$), activation energy ($E_a$), and the Arrhenius prefactor ($\ln A$) for polymer electrolytes. It uses a physics-informed architecture where the output is constrained by the Arrhenius equation:
+
+    $$ \ln \sigma = \ln A - \frac{E_a}{RT} $$
+
+    ## Usage
+
+    This model expects inputs processed via `MolGraphizer` and `expand_polymer_smiles`.
+    It requires the following features:
+    - **Molecular Graph**: Nodes, Edges, Connectivity.
+    - **Auxiliary Features**: Temperature (K), Log Molecular Weight, Molality.
+
+    To use this model for screening new candidates, use the `screen_from_hub.py` script provided in the repository.
+
+    ```python
+    # Pseudo-code for loading
+    import flax.serialization
+    from huggingface_hub import hf_hub_download
+
+    path = hf_hub_download(repo_id="eamag/chemarr", filename="model.msgpack")
+    with open(path, "rb") as f:
+        artifact = flax.serialization.from_bytes(dummy_state, f.read())
+
+