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license: other
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
---
# Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation
Dataset containing 137 perovskite thermal expansion coefficients from experiment
## Dataset Information
- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/avvr-p174](https://doi.org/10.18126/avvr-p174)
- **Year**: 2023
- **Authors**: McGuinness, Kevin P., Oliynyk, Anton O., Lee, Sangjoon, Molero-Sanchez, Beatriz, Addo, Paul Kwesi
- **Data Type**: tabular
### Fields
| Field | Role | Description | Units |
|-------|------|-------------|-------|
| Composition | input | Material composition with sites | |
| Initial Temperature (degC) | input | Initial temperature | degC |
| Final Temperature (degC) | input | Final temperature | degC |
| TEC (x10^-6 K^-1) | target | Thermal expansion coefficient | K^-1 |
### 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/avvr-p174")
X, y = dataset.get_as_dict()['train']
```
### With HuggingFace Datasets
```python
from datasets import load_dataset
dataset = load_dataset("Dataset_perovskite_tec")
```
## Citation
```bibtex
@misc{https://doi.org/10.18126/avvr-p174
doi = {10.18126/avvr-p174}
url = {https://doi.org/10.18126/avvr-p174}
author = {McGuinness, Kevin P. and Oliynyk, Anton O. and Lee, Sangjoon and Molero-Sanchez, Beatriz and Addo, Paul Kwesi}
title = {Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2023}}
```
## License
other
---
*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*
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