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---
license: cc-by-4.0
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
---
# Exploring effective charge in electromigration using machine learning
Dataset containing effective charge values for 49 metal host-impurity pairs
## Dataset Information
- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/abxi-r7eb](https://doi.org/10.18126/abxi-r7eb)
- **Year**: 2022
- **Authors**: Liu, Yu-chen, Afflerbach, Ben, Jacobs, Ryan, Lin, Shih-kang, Morgan, Dane
- **Data Type**: tabular
### Fields
| Field | Role | Description | Units |
|-------|------|-------------|-------|
| Impurity | input | Impurity element | |
| Host | input | Host element | |
| Effective_charge_regression | target | Alloy effective charge values (target) | |
### 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/abxi-r7eb")
X, y = dataset.get_as_dict()['train']
```
### With HuggingFace Datasets
```python
from datasets import load_dataset
dataset = load_dataset("electromigration_v1.1")
```
## Citation
```bibtex
@misc{https://doi.org/10.18126/abxi-r7eb
doi = {10.18126/abxi-r7eb}
url = {https://doi.org/10.18126/abxi-r7eb}
author = {Liu, Yu-chen and Afflerbach, Ben and Jacobs, Ryan and Lin, Shih-kang and Morgan, Dane}
title = {Exploring effective charge in electromigration using machine learning}
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.*
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