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@@ -75,7 +75,47 @@ Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi
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# Model card - RandomForestMetalClassifier
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-
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# Citation
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doi = {10.1103/PhysRevLett.120.145301},
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url = {https://link.aps.org/doi/10.1103/PhysRevLett.120.145301}
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}
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```
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# Model card - RandomForestMetalClassifier
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**Model Details**: A RandomForest model to classify whether a crystal could be a metal or nonmetal.
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**Developers**: [SemiconAI repo's](https://github.com/dilangaem/SemiconAI) developers.
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**Distributors**: Original authors' code wrapped and distributed by GT4SD Team (2023) from IBM Research.
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**Model date**: 2022.
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**Algorithm version**: Models trained and distributed by the original authors.
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- **Metal NonMetal Classifier**: Model trained to classify whether a crystal could be metal or nonmetal.
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**Model type**: A metal/nonmetal classifier for crystals based on the RandomForest algorithm.
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**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**:
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See the [original paperl](https://www.nature.com/articles/s41524-022-00850-3) for details.
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**Paper or other resource for more information**:
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The [original paper](https://www.nature.com/articles/s41524-022-00850-3). See the [source code](https://github.com/dilangaem/SemiconAI) for details.
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**License**: MIT
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**Where to send questions or comments about the model**: Open an issue on [SemiconAI](https://github.com/dilangaem/SemiconAI) repo.
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**Intended Use. Use cases that were envisioned during development**: Materials research.
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**Primary intended uses/users**: Researchers using the model for model comparison or research exploration purposes.
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**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
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**Factors**: N.A.
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**Metrics**: N.A.
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**Datasets**: See **Algorithm version**.
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**Ethical Considerations**: No specific considerations as no private/personal data is involved. Please consult with the authors in case of questions.
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**Caveats and Recommendations**: Please consult with original authors in case of questions.
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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)
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# Citation
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doi = {10.1103/PhysRevLett.120.145301},
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url = {https://link.aps.org/doi/10.1103/PhysRevLett.120.145301}
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}
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@article{siriwardane2022generative,
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title={Generative design of stable semiconductor materials using deep learning and density functional theory},
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author={Siriwardane, Edirisuriya M Dilanga and Zhao, Yong and Perera, Indika and Hu, Jianjun},
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journal={npj Computational Materials},
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volume={8},
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number={1},
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pages={164},
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year={2022},
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publisher={Nature Publishing Group UK London}
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}
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```
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