Material compositions 1
stringclasses 15
values | Material compositions 2
stringclasses 58
values | E_regression_shift
float64 -0.94
1.94
|
|---|---|---|
Ag
|
Ag
| 0
|
Ag
|
Co
| -0.090142
|
Ag
|
Cr
| 0.259139
|
Ag
|
Cu
| -0.0222
|
Ag
|
Fe
| 0.317672
|
Ag
|
Mn
| 0.202186
|
Ag
|
Ni
| 0.250571
|
Ag
|
Sc
| -0.001431
|
Ag
|
Ti
| 0.164968
|
Ag
|
V
| 0.248163
|
Ag
|
Zn
| -0.146976
|
Al
|
Al
| 0
|
Al
|
Ag
| -0.12503
|
Al
|
As
| -0.14243
|
Al
|
Au
| -0.22471
|
Al
|
Bi
| -0.35403
|
Al
|
Ca
| -0.19265
|
Al
|
Cd
| -0.10511
|
Al
|
Ce
| -0.33259
|
Al
|
Co
| 0.7443
|
Al
|
Cr
| 0.9101
|
Al
|
Cu
| -0.07002
|
Al
|
Er
| -0.10337
|
Al
|
Fe
| 0.8777
|
Al
|
Ga
| -0.07761
|
Al
|
Ge
| -0.11104
|
Al
|
Hf
| 0.79709
|
Al
|
Hg
| -0.18024
|
Al
|
In
| -0.16474
|
Al
|
Ir
| 0.9591
|
Al
|
La
| -0.48849
|
Al
|
Mg
| -0.01939
|
Al
|
Mn
| 0.6552
|
Al
|
Mo
| 1.7601
|
Al
|
Na
| -0.06923
|
Al
|
Nb
| 1.4009
|
Al
|
Nd
| -0.3353
|
Al
|
Ni
| 0.33349
|
Al
|
Os
| 1.54
|
Al
|
P
| -0.22713
|
Al
|
Pb
| -0.32056
|
Al
|
Pd
| 0.25651
|
Al
|
Pt
| 0.33779
|
Al
|
Re
| 1.9229
|
Al
|
Rh
| 0.8024
|
Al
|
Ru
| 1.3302
|
Al
|
S
| -0.15472
|
Al
|
Sb
| -0.24038
|
Al
|
Sc
| 0.30159
|
Al
|
Se
| -0.44259
|
Al
|
Si
| -0.15373
|
Al
|
Sn
| -0.1955
|
Al
|
Ta
| 1.5159
|
Al
|
Tc
| 1.7083
|
Al
|
Te
| -0.36132
|
Al
|
Ti
| 0.94129
|
Al
|
Tl
| -0.27676
|
Al
|
V
| 1.3412
|
Al
|
W
| 1.9362
|
Al
|
Y
| -0.14998
|
Al
|
Zn
| -0.08491
|
Al
|
Zr
| 0.65613
|
Au
|
Au
| 0
|
Au
|
Ag
| 0.0348
|
Au
|
Cd
| -0.0552
|
Au
|
Mo
| 1.0278
|
Au
|
Nb
| 0.978
|
Au
|
Pd
| 0.4194
|
Au
|
Rh
| 0.7576
|
Au
|
Ru
| 0.9326
|
Au
|
Tc
| 1.0052
|
Au
|
Y
| 0.0704
|
Au
|
Zr
| 0.6971
|
Ca
|
Ca
| 0
|
Ca
|
Ag
| -0.11076
|
Ca
|
Cd
| -0.03195
|
Ca
|
Nb
| 0.848
|
Ca
|
Y
| 0.29815
|
Ca
|
Zr
| 0.63619
|
Cu
|
Cu
| 0
|
Cu
|
Ag
| -0.1328
|
Cu
|
Al
| -0.05359
|
Cu
|
Au
| -0.10813
|
Cu
|
Bi
| -0.44205
|
Cu
|
Cd
| -0.18004
|
Cu
|
Co
| 0.39165
|
Cu
|
Cr
| 0.07129
|
Cu
|
Fe
| 0.39712
|
Cu
|
Ga
| -0.1059
|
Cu
|
Ge
| -0.17374
|
Cu
|
Hf
| -0.15578
|
Cu
|
Hg
| -0.22938
|
Cu
|
In
| -0.20489
|
Cu
|
Ir
| 0.59565
|
Cu
|
Mg
| -0.09191
|
Cu
|
Mn
| -0.04548
|
Cu
|
Mo
| 0.15031
|
Cu
|
Nb
| -0.08554
|
Cu
|
Ni
| 0.27892
|
Cu
|
Os
| 0.64888
|
End of preview. Expand
in Data Studio
Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion
Dataset containing DFT-calculated dilute alloy impurity diffusion barriers for 408 host-impurity pairs
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/uppe-p8p1
- Year: 2022
- Authors: Lu, Haijin, Zou, Nan, Jacobs, Ryan, Afflerbach, Ben, Lu, Xiao-Gang, Morgan, Dane
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| Material compositions 1 | input | Host element | |
| Material compositions 2 | input | Solute element | |
| E_regression_shift | target | DFT-calculated solute migration barrier, given rel | eV |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/uppe-p8p1")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("diffusion_v1.4")
Citation
@misc{https://doi.org/10.18126/uppe-p8p1
doi = {10.18126/uppe-p8p1}
url = {https://doi.org/10.18126/uppe-p8p1}
author = {Lu, Haijin and Zou, Nan and Jacobs, Ryan and Afflerbach, Ben and Lu, Xiao-Gang and Morgan, Dane}
title = {Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion}
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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