README.md

---
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.*