# Model documentation & parameters

## Parameters

### Model
Whether to use the model trained 1) on procedures for heterogeneous single-atom catalyst synthesis, or 2) on organic chemistry procedures.

### Synthesis text
Synthesis procedure (in English prose) to extract actions from.


# Model card -- Text mining synthesis protocols of heterogeneous single-atom catalysts

**Model Details**: 
Sequence-to-sequence transformer model 

**Developers**: 
Manu Suvarna, Alain C. Vaucher, Sharon Mitchell, Teodoro Laino, and Javier Pérez-Ramírez.

**Distributors**:
Same as the *developers*.

**Model date**: 
April 2023.

**Algorithm version**: 
Details in the source code and in the paper.

**Model type**: 
A Transformer-based sequence-to-sequence language model that extracts synthesis actions from procedure text.
The model relies on the [OpenNMT](https://github.com/OpenNMT/OpenNMT-py) library.

**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**:
Details in the source code and in the paper.

**Paper or other resource for more information**: 
Currently under review.

**License**: MIT

**Where to send questions or comments about the model**: 
Contact one of the *developers*.

**Intended Use. Use cases that were envisioned during development**: 
Chemical research, in particular in the field of heterogeneous single-atom catalysts.

**Primary intended uses/users**: 
Researchers and computational chemists using the model for model comparison or research exploration purposes.

**Out-of-scope use cases**: 
Production-level inference.

**Factors**: 
Not applicable.

**Metrics**: 
Details in the source code and in the paper.

**Datasets**: 
Details in the source code and in the paper.

**Ethical Considerations**: 
No specific considerations as no private/personal data is involved. 
Please consult with the authors in case of questions.

**Caveats and Recommendations**: 
Please consult with original authors in case of questions.

Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596).


## Citation

```bib
@article{suvarna2023textmining,
  title={Text mining and standardization of single-atom catalyst protocols to foster digital synthesis},
  author={Manu Suvarna, Alain C. Vaucher, Sharon Mitchell, Teodoro Laino, and Javier Pérez-Ramírez},
  journal={under review},
}
```