formula
stringlengths 2
15
| target
float64 0
0
|
|---|---|
Ho2In1Ni2
| 0.00005
|
La1Se1
| 0.000046
|
Mn1Si1Tb1
| 0.000044
|
Cs1H1
| 0.000393
|
Ag1Al1Se2
| 0.000071
|
Pd1Sb1Tb1
| 0.000045
|
Ge1Li1Y1
| 0.000063
|
Ag1As1S1
| 0.000078
|
Hf1Rh1Si1
| 0.000027
|
Er1In1Pt1
| 0.000042
|
Ir2P1
| 0.000024
|
Al2Ni1Y1
| 0.000045
|
Pt5Se4
| 0.00004
|
Ag1Er1
| 0.000066
|
P2Zn3
| 0.000072
|
Th3Tl5
| 0.000054
|
P1Ru1Zr1
| 0.000026
|
As1Li1Zn1
| 0.000095
|
Au1Ge1Ho1
| 0.000051
|
C1Re1
| 0.000018
|
Ca2Pb1
| 0.000086
|
Ba1N2Zr1
| 0.000036
|
Mo2Zr1
| 0.000022
|
Co2Fe1In1
| 0.000061
|
As1Hf1
| 0.000032
|
Er1Pt2Si2
| 0.000034
|
Dy1Hg1
| 0.000055
|
B6Ni21U2
| 0.00004
|
Li4O4Pb1
| 0.000091
|
Au1Er1Ni4
| 0.000061
|
Pd1Yb1
| 0.000068
|
Sn1Zr3
| 0.000034
|
Bi2Ho6Rh1
| 0.000045
|
Pt1Sn1Y1
| 0.000041
|
Re2Sc1
| 0.000021
|
C1Ru3Ta1
| 0.000021
|
Al1Pd1
| 0.000043
|
Ho1In1Zn1
| 0.000062
|
Li1Pb1Pd2
| 0.000084
|
Al2Y3
| 0.000045
|
F3Y1
| 0.000055
|
Ni1Sb2
| 0.000053
|
Al4In3Sr11
| 0.000089
|
Pb1S1
| 0.000062
|
As1Hg1K1
| 0.000123
|
Be2Ti1
| 0.000039
|
Al1Ge1Sr1
| 0.000084
|
C1Ni2W4
| 0.000021
|
Ni1Si1Th1
| 0.000033
|
Hf1Pd5
| 0.00004
|
As3Yb4
| 0.000072
|
Al16Hf6Pd7
| 0.000039
|
Ba1P2Ru2
| 0.000034
|
B6Sr1
| 0.00003
|
Te1Zn1
| 0.00008
|
Er1Rh5
| 0.000032
|
Au1C2Cs1
| 0.000059
|
Pd2Si1Tb1
| 0.000042
|
Cd3N2
| 0.000063
|
Au1Ca1In2
| 0.000096
|
Cr2Cu1S4
| 0.000059
|
O6Os2Rb1
| 0.000029
|
Li1Pd2Sn6
| 0.000073
|
Ru3Si2Y1
| 0.000031
|
Hf1Pt1Si1
| 0.000028
|
Ni1Zr1
| 0.000035
|
K1Zn13
| 0.00013
|
Ga1Pd2Sc1
| 0.00005
|
Ge1Y1Zn1
| 0.000054
|
Pb13Rh4Sr3
| 0.000068
|
Ir3Th7
| 0.000028
|
Au1Rb1
| 0.000349
|
O6Pt3Zn1
| 0.000039
|
Ge2Sc1
| 0.000053
|
As1Ga1
| 0.000068
|
Au1Dy1Pb1
| 0.000056
|
Tc1Ti1
| 0.000025
|
Nb4O5
| 0.000024
|
H2Sr1
| 0.000131
|
As2Cu1Y1
| 0.00005
|
Al1F4Na1
| 0.000065
|
C2Dy1Ni1
| 0.000033
|
N1Os1
| 0.000022
|
In1Ni2Zr1
| 0.000046
|
Se4Y2Zn1
| 0.000051
|
Ce1Rh3
| 0.000032
|
N1Re2
| 0.000018
|
In1Pt1Y1
| 0.000042
|
Sr1Tl2
| 0.000136
|
Cu4O3
| 0.000064
|
Ba1O7U2
| 0.000033
|
Re1Si1Ti1
| 0.000023
|
Ti1Zn3
| 0.000088
|
Pt1Sn2
| 0.000059
|
Cd1Pd1
| 0.000065
|
Cd2Sr1
| 0.000124
|
As1Pu1
| 0.00006
|
Au1Si1Y1
| 0.000045
|
Mn1Pd1Te1
| 0.000061
|
Ba1Li1Sb1
| 0.00009
|
End of preview. Expand
in Data Studio
Benchmark AFLOW Data Sets for Machine Learning (Thermal expansion)
Dataset containing 4886 thermal expansion coefficients
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/qmrs-jg02
- Year: 2020
- Authors: Clement, Conrad L., Kauwe, Steven K., Sparks, Taylor D.
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| formula | input | Material composition | |
| target | target | Thermal expansion coefficient | K^-1 |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/qmrs-jg02")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("Dataset_thermalexp_aflow")
Citation
@misc{https://doi.org/10.18126/qmrs-jg02
doi = {10.18126/qmrs-jg02}
url = {https://doi.org/10.18126/qmrs-jg02}
author = {Clement, Conrad L. and Kauwe, Steven K. and Sparks, Taylor D.}
title = {Benchmark AFLOW Data Sets for Machine Learning (Thermal expansion)}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2020}}
License
other
This dataset was exported from Foundry-ML, a platform for materials science datasets.
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