metadata
license: cc-by-4.0
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
University of Alabama Heusler database
Dataset containing saturation magnetization values of 1153 Heusler compounds
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/un63-ohqv
- Year: 2022
- Authors: Borg, Chris
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| formula | input | Material composition | |
| heusler type | input | Type of Heusler structure | |
| num_electron | input | Number of electrons in Heusler structure | |
| struct type | input | The structure type | |
| latt const | input | Lattice constant | Angstroms |
| tetragonality | input | The structure tetragonality factor (c/a) | |
| e_form | input | Formation energy | eV/atom |
| pol fermi | input | Polarization at Fermi level | % |
| mu_b | input | Magnetic moment of structure | Bohr magneton |
| mu_b saturation | target | Saturation magnetization | emu/cc |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/un63-ohqv")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("heusler_magnetization_v1.1")
Citation
@misc{https://doi.org/10.18126/un63-ohqv
doi = {10.18126/un63-ohqv}
url = {https://doi.org/10.18126/un63-ohqv}
author = {Borg, Chris}
title = {University of Alabama Heusler database}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}
License
CC-BY 4.0
This dataset was exported from Foundry-ML, a platform for materials science datasets.