Update model_cards/article.md

model_cards/article.md

@@ -75,7 +75,47 @@ Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi
 
 # Model card - RandomForestMetalClassifier
 
-ToDo...
+**Model Details**: A RandomForest model to classify whether a crystal could be a metal or nonmetal. 
+
+**Developers**: [SemiconAI repo's](https://github.com/dilangaem/SemiconAI) developers.
+
+**Distributors**: Original authors' code wrapped and distributed by GT4SD Team (2023) from IBM Research.
+
+**Model date**: 2022.
+
+**Algorithm version**: Models trained and distributed by the original authors.
+- **Metal NonMetal Classifier**: Model trained to classify whether a crystal could be metal or nonmetal.
+ 
+**Model type**: A metal/nonmetal classifier for crystals based on the RandomForest algorithm.
+
+**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: 
+See the [original paperl](https://www.nature.com/articles/s41524-022-00850-3) for details.
+
+**Paper or other resource for more information**: 
+The [original paper](https://www.nature.com/articles/s41524-022-00850-3). See the [source code](https://github.com/dilangaem/SemiconAI) for details.
+
+**License**: MIT
+
+**Where to send questions or comments about the model**: Open an issue on [SemiconAI](https://github.com/dilangaem/SemiconAI) repo.
+
+**Intended Use. Use cases that were envisioned during development**: Materials research.
+
+**Primary intended uses/users**: Researchers using the model for model comparison or research exploration purposes.
+
+**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
+
+**Factors**: N.A.
+
+**Metrics**: N.A.
+
+**Datasets**: See **Algorithm version**.
+
+**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?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
+
 
 
 # Citation
@@ -95,4 +135,15 @@ ToDo...
   doi = {10.1103/PhysRevLett.120.145301},
   url = {https://link.aps.org/doi/10.1103/PhysRevLett.120.145301}
 }
+
+@article{siriwardane2022generative,
+  title={Generative design of stable semiconductor materials using deep learning and density functional theory},
+  author={Siriwardane, Edirisuriya M Dilanga and Zhao, Yong and Perera, Indika and Hu, Jianjun},
+  journal={npj Computational Materials},
+  volume={8},
+  number={1},
+  pages={164},
+  year={2022},
+  publisher={Nature Publishing Group UK London}
+}
 ```