README.md

---
license: other
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - materials-science
  - chemistry
  - foundry-ml
  - scientific-data
size_categories:
  - 1K<n<10K
---

# The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Dataset containing calculated exfoliation energies for 636 materials

## Dataset Information

- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/yx4h-ny59](https://doi.org/10.18126/yx4h-ny59)
- **Year**: 2020
- **Authors**: Choudhary, Kamal, Garrity, Kevin F., Reid, Andrew C. E., DeCost, Brian, Biacchi, Adam J., Walker, Angela R. Hight, Trautt, Zachary, Hattrick-Simpers, Jason, Kusne, A. Gilad, Centrone, Andrea, Davydov, Albert, Jiang, Jie, Pachter, Ruth, Cheon, Gowoon, Reed, Evan, Agrawal, Ankit, Qian, Xiaofeng, Sharma, Vinit, Zhuang, Houlong, Kalinin, Sergei V., Sumpter, Bobby G., Pilania, Ghanshyam, Acar, Pinar, Mandal, Subhasish, Haule, Kristjan, Vanderbilt, David, Rabe, Karin, Tavazza, Francesca
- **Data Type**: tabular

### Fields

| Field | Role | Description | Units |
|-------|------|-------------|-------|
| formula | input | Material composition |  |
| target | target | Exfoliation energy | eV/atom |


### Splits

- **train**: train


## Usage

### With Foundry-ML (recommended for materials science workflows)

```python
from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/yx4h-ny59")
X, y = dataset.get_as_dict()['train']
```

### With HuggingFace Datasets

```python
from datasets import load_dataset

dataset = load_dataset("Dataset_exfoliationE")
```

## Citation

```bibtex
@misc{https://doi.org/10.18126/yx4h-ny59
doi = {10.18126/yx4h-ny59}
url = {https://doi.org/10.18126/yx4h-ny59}
author = {Choudhary, Kamal and Garrity, Kevin F. and Reid, Andrew C. E. and DeCost, Brian and Biacchi, Adam J. and Walker, Angela R. Hight and Trautt, Zachary and Hattrick-Simpers, Jason and Kusne, A. Gilad and Centrone, Andrea and Davydov, Albert and Jiang, Jie and Pachter, Ruth and Cheon, Gowoon and Reed, Evan and Agrawal, Ankit and Qian, Xiaofeng and Sharma, Vinit and Zhuang, Houlong and Kalinin, Sergei V. and Sumpter, Bobby G. and Pilania, Ghanshyam and Acar, Pinar and Mandal, Subhasish and Haule, Kristjan and Vanderbilt, David and Rabe, Karin and Tavazza, Francesca}
title = {The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2020}}
```

## License

other

---

*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*