metadata
license: other
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
Metallic Glasses and their Properties
Dataset containing experimental max casting diameters of 998 metallic glasses
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/fs5e-kr15
- Year: 2021
- Authors: Voyles, Paul M, Schultz, Lane E., Morgan, Dane, Francis, Carter, Afflerbach, Benjamin, Hakeem, Abdulrhman
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| Composition | input | Material composition | |
| Reference | input | Original data reference | |
| Tg_[K] | input | Glass transition temperature | K |
| Tx_[K] | input | Crystallization temperature | K |
| Tl_[K] | input | Liquidus temperature | K |
| Zmax_[mm] | input | Maximum casting size | mm |
| Dmax_[mm] | target | Maximum casting diameter | mm |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/fs5e-kr15")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("Dataset_metallicglass_Dmax")
Citation
@misc{https://doi.org/10.18126/fs5e-kr15
doi = {10.18126/fs5e-kr15}
url = {https://doi.org/10.18126/fs5e-kr15}
author = {Voyles, Paul M and Schultz, Lane E. and Morgan, Dane and Francis, Carter and Afflerbach, Benjamin and Hakeem, Abdulrhman}
title = {Metallic Glasses and their Properties}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2021}}
License
other
This dataset was exported from Foundry-ML, a platform for materials science datasets.