Datasets:
File size: 1,928 Bytes
a2cec24 5259b6a a2cec24 5259b6a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
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.*
|