---
    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())