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# Model documentation & parameters
## Parameters
### Property
The supported properties are:
- `Metal NonMetal Classifier`: Classifying whether a crystal could be metal or nonmetal using a [RandomForest classifier](https://www.nature.com/articles/s41524-022-00850-3)
- `Metal Semiconductor Classifier`: Classifying whether a crystal could be metal or semiconductor using the [CGCNN framework](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301).
- `Poisson Ratio`: Predicted using the [CGCNN framework](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301).
- `Shear Moduli`: Predicted using the [CGCNN framework](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301).
- `Bulk Moduli`: Predicted using the [CGCNN framework](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301).
- `Fermi Energy`: Predicted using the [CGCNN framework](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301).
- `Band Gap`: Predicted using the [CGCNN framework](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301).
- `Absolute Energy`: Predicted using the [CGCNN framework](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301).
- `Formation Energy`: Predicted using the [CGCNN framework](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301).
### Input file for crystal model
The file with information about the metal. Dependent on the property you want to predict, the format of the file differs:
- `Metal NonMetal Classifier`. It requires a single `.csv` file with the metal (chemical formula) in the first column and the crystal system in the second.
- **All others**: Predicted with CGCNN. The input can either be a single `.cif` file (to predict a single metal) or a `.zip` folder which contains multiple `.cif` (for batch prediction)
# Model card - CGCNN
**Model Details**: Eight CGCNN models trained to predict various properties for crystals.
**Developers**: [CGCNN's](https://github.com/txie-93/cgcnn) developers.
**Distributors**: Original authors' code wrapped and distributed by GT4SD Team (2023) from IBM Research.
**Model date**: 2018.
**Algorithm version**: Models trained and distributed by the original authors.
- **Metal Semiconductor Classifier**: Model trained to classify whether a crystal could be metal or semiconductor using instances from this [database](https://aip.scitation.org/doi/10.1063/1.4812323) that includes a diverse set of inorganic crystals ranging from simple metals to complex minerals..
- **Poisson Ratio**: Model to predict the Poisson ratio trained on 2041 instances from this [database](https://aip.scitation.org/doi/10.1063/1.4812323) that includes a diverse set of inorganic crystals ranging from simple metals to complex minerals.
- **Shear Moduli**: Model to predict the Shear moduli trained on 2041 instances from this [database](https://aip.scitation.org/doi/10.1063/1.4812323) that includes a diverse set of inorganic crystals ranging from simple metals to complex minerals. Unit log(GPa).
- **Bulk Moduli**: Model to predict the Bulk moduli trained on 2041 instances from this [database](https://aip.scitation.org/doi/10.1063/1.4812323) that includes a diverse set of inorganic crystals ranging from simple metals to complex minerals. Unit log(GPa).
- **Fermi Energy**: Model to predict the Fermi energy trained on 28046 instances from this [database](https://aip.scitation.org/doi/10.1063/1.4812323) that includes a diverse set of inorganic crystals ranging from simple metals to complex minerals. Unit eV.
- **Band Gap**: Model to predict the Band Gap trained on 16458 instances from this [database](https://aip.scitation.org/doi/10.1063/1.4812323) that includes a diverse set of inorganic crystals ranging from simple metals to complex minerals. Unit eV.
- **Absolute Energy**: Model to predict the Absolute energy trained on 28046 instances from this [database](https://aip.scitation.org/doi/10.1063/1.4812323) that includes a diverse set of inorganic crystals ranging from simple metals to complex minerals. Unit eV/atom.
- **Formation Energy**: Model to predict the formation energy trained on 28046 instances from this [database](https://aip.scitation.org/doi/10.1063/1.4812323) that includes a diverse set of inorganic crystals ranging from simple metals to complex minerals. Unit eV/atom.
**Model type**: Crystal Graph Convolutional Neural Networks (CGCNN) that take an arbitary crystal structure to predict material properties.
**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**:
See the [CGCNN](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301) paper for details.
**Paper or other resource for more information**:
The [CGCNN](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301) paper. See the [source code](https://github.com/txie-93/cgcnn) for details.
**License**: MIT
**Where to send questions or comments about the model**: Open an issue on [CGCNN](https://github.com/txie-93/cgcnn) 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**: Different ones, as described under **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)
# Model card - RandomForestMetalClassifier
**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
```bib
@article{PhysRevLett.120.145301,
title = {Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties},
author = {Xie, Tian and Grossman, Jeffrey C.},
journal = {Phys. Rev. Lett.},
volume = {120},
issue = {14},
pages = {145301},
numpages = {6},
year = {2018},
month = {Apr},
publisher = {American Physical Society},
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}
}
``` |