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
---

# Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion

Dataset containing DFT-calculated dilute alloy impurity diffusion barriers for 408 host-impurity pairs

## Dataset Information

- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/uppe-p8p1](https://doi.org/10.18126/uppe-p8p1)
- **Year**: 2022
- **Authors**: Lu, Haijin, Zou, Nan, Jacobs, Ryan, Afflerbach, Ben, Lu, Xiao-Gang, Morgan, Dane
- **Data Type**: tabular

### Fields

| Field | Role | Description | Units |
|-------|------|-------------|-------|
| Material compositions 1 | input | Host element |  |
| Material compositions 2 | input | Solute element |  |
| E_regression_shift | target | DFT-calculated solute migration barrier, given rel | eV |


### 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/uppe-p8p1")
X, y = dataset.get_as_dict()['train']
```

### With HuggingFace Datasets

```python
from datasets import load_dataset

dataset = load_dataset("diffusion_v1.4")
```

## Citation

```bibtex
@misc{https://doi.org/10.18126/uppe-p8p1
doi = {10.18126/uppe-p8p1}
url = {https://doi.org/10.18126/uppe-p8p1}
author = {Lu, Haijin and Zou, Nan and Jacobs, Ryan and Afflerbach, Ben and Lu, Xiao-Gang and Morgan, Dane}
title = {Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion}
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.*