---
license: cc-by-4.0
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - materials-science
  - chemistry
  - foundry-ml
  - scientific-data
size_categories:
  - 1K<n<10K
---

# Machine learning modeling of superconducting critical temperature

Dataset containing experimentally measured superconducting critical temperatures for 16414 materials

## Dataset Information

- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/xlfr-hjrn](https://doi.org/10.18126/xlfr-hjrn)
- **Year**: 2022
- **Authors**: Stanev, Valentin, Oses, Corey, Kusne, A. Gilad, Rodriguez, Efrain, Paglione, Johnpierre, Curtarolo, Stefano, Takeuchi, Ichiro
- **Data Type**: tabular

### Fields

| Field | Role | Description | Units |
|-------|------|-------------|-------|
| name | input | Material composition |  |
| Tc | target | Experimental superconducting critical temperature | K |


### 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/xlfr-hjrn")
X, y = dataset.get_as_dict()['train']
```

### With HuggingFace Datasets

```python
from datasets import load_dataset

dataset = load_dataset("superconductivity_v1.1")
```

## Citation

```bibtex
@misc{https://doi.org/10.18126/xlfr-hjrn
doi = {10.18126/xlfr-hjrn}
url = {https://doi.org/10.18126/xlfr-hjrn}
author = {Stanev, Valentin and Oses, Corey and Kusne, A. Gilad and Rodriguez, Efrain and Paglione, Johnpierre and Curtarolo, Stefano and Takeuchi, Ichiro}
title = {Machine learning modeling of superconducting critical temperature}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}
```

## License

CC-BY 4.0

---

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