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foundry-ml/dataset_mg_alloy

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 DOI: 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… See the full description on the dataset page: https://huggingface.co/datasets/foundry-ml/dataset_mg_alloy.

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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
—DOI: 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.