README.md

---
license: other
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - materials-science
  - chemistry
  - foundry-ml
  - scientific-data
size_categories:
  - 1K<n<10K
---

# Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning

Dataset containing mechanical properties of 365 Mg alloys

## Dataset Information

- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/myj4-0h48](https://doi.org/10.18126/myj4-0h48)
- **Year**: 2023
- **Authors**: Hou, Haobing, Wang, Jianfeng, Ye, Li, Zhu, Shijie, Wang, Liguo, Guan, Shaokang
- **Data Type**: tabular

### Fields

| Field | Role | Description | Units |
|-------|------|-------------|-------|
| Mg(wt.%) | input | Amount of Mg | wt% |
| Zn(wt.%) | input | Amount of Zn | wt% |
| Y(wt.%) | input | Amount of Y | wt% |
| Zr(wt.%) | input | Amount of Zr | wt% |
| Nd(wt.%) | input | Amount of Nd | wt% |
| Gd(wt.%) | input | Amount of Gd | wt% |
| solution temperature(°Ê) | input | Solution temperature | degC |
| solution time(h) | input | Solution time | hours |
| homogenization temperature(°Ê) | input | Homogenization temperature | degC |
| homogenization time(h) | input | Homogenization time | hours |
| extrusion temperature(°Ê) | input | Extrusion temperature | degC |
| extrusion ratio | input | Extrusion ratio |  |
| aging temperature(°Ê) | input | Aging temperature | degC |
| aging time(h) | input | Aging time | hours |
| UTS(MPa) | target | Ultimate tensile strength | MPa |
| YS(MPa) | target | Yield strength | MPa |
| EL(%) | target | Elongation |  |


### 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/myj4-0h48")
X, y = dataset.get_as_dict()['train']
```

### With HuggingFace Datasets

```python
from datasets import load_dataset

dataset = load_dataset("Dataset_Mg_alloy")
```

## Citation

```bibtex
@misc{https://doi.org/10.18126/myj4-0h48
doi = {10.18126/myj4-0h48}
url = {https://doi.org/10.18126/myj4-0h48}
author = {Hou, Haobing and Wang, Jianfeng and Ye, Li and Zhu, Shijie and Wang, Liguo and Guan, Shaokang}
title = {Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning}
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.*