Impurity
string | Host
string | Effective_charge_regression
float64 |
|---|---|---|
Ag
|
Ag
| -8.3
|
Cd
|
Ag
| -29.1
|
Cu
|
Ag
| -16.1
|
Fe
|
Ag
| -58
|
In
|
Ag
| -40
|
Sb
|
Ag
| -90
|
Sn
|
Ag
| -60
|
Zn
|
Ag
| -27.6
|
Al
|
Al
| -30
|
Cu
|
Al
| -6.8
|
Mg
|
Al
| -52.4
|
Zn
|
Al
| -33.9
|
Au
|
Au
| -9
|
In
|
Au
| -22.2
|
Sn
|
Au
| -32.6
|
Cd
|
Cd
| -5.1
|
Co
|
Co
| 1.6
|
Cr
|
Cr
| 3
|
Ag
|
Cu
| -20.7
|
Cu
|
Cu
| -5.5
|
Fe
|
Cu
| -61.3
|
Zn
|
Cu
| -23.3
|
Fe
|
Fe
| 2
|
Ga
|
Ga
| -1.3
|
In
|
In
| -11.5
|
Li
|
Li
| -2.5
|
Mg
|
Mg
| 2
|
Na
|
Na
| -3.3
|
Cr
|
Nb
| -2
|
Fe
|
Nb
| -2.9
|
Nb
|
Nb
| 2.7
|
Ni
|
Ni
| -3.5
|
Ag
|
Pb
| 0.7
|
Au
|
Pb
| -2.05
|
Cd
|
Pb
| 2.5
|
Cu
|
Pb
| 1.07
|
Ni
|
Pb
| -6.75
|
Pb
|
Pb
| -16.6
|
Zn
|
Pb
| 0.2
|
Pd
|
Pd
| 1.3
|
Pt
|
Pt
| 0.28
|
Ag
|
Sn
| -2.15
|
Au
|
Sn
| -9.5
|
Ni
|
Sn
| -40
|
Sn
|
Sn
| -18
|
Tl
|
Tl
| -4
|
U
|
U
| -1.6
|
Zn
|
Zn
| -4.3
|
Zr
|
Zr
| 0.3
|
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.
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