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
Exploring effective charge in electromigration using machine learning
Dataset containing effective charge values for 49 metal host-impurity pairs
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/abxi-r7eb
- Year: 2022
- Authors: Liu, Yu-chen, Afflerbach, Ben, Jacobs, Ryan, Lin, Shih-kang, Morgan, Dane
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| Impurity | input | Impurity element | |
| Host | input | Host element | |
| Effective_charge_regression | target | Alloy effective charge values (target) |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/abxi-r7eb")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("electromigration_v1.1")
Citation
@misc{https://doi.org/10.18126/abxi-r7eb
doi = {10.18126/abxi-r7eb}
url = {https://doi.org/10.18126/abxi-r7eb}
author = {Liu, Yu-chen and Afflerbach, Ben and Jacobs, Ryan and Lin, Shih-kang and Morgan, Dane}
title = {Exploring effective charge in electromigration using machine learning}
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.