foundry-ml/electromigration_v1-1

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

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This dataset was exported from Foundry-ML, a platform for materials science datasets.