formula
stringlengths 2
15
| target
float64 0.01
205
|
|---|---|
Ho2In1Ni2
| 4.07935
|
La1Se1
| 4.97254
|
Mn1Si1Tb1
| 5.22674
|
Cs1H1
| 0.976334
|
Ag1Al1Se2
| 1.29014
|
Pd1Sb1Tb1
| 2.59722
|
Ge1Li1Y1
| 5.55952
|
Ag1As1S1
| 0.708433
|
Hf1Rh1Si1
| 7.28241
|
Er1In1Pt1
| 1.40254
|
Ir2P1
| 12.7858
|
Al2Ni1Y1
| 6.69616
|
Pt5Se4
| 1.33708
|
Ag1Er1
| 3.68224
|
P2Zn3
| 2.99174
|
Th3Tl5
| 0.639532
|
P1Ru1Zr1
| 8.34587
|
As1Li1Zn1
| 7.48874
|
Au1Ge1Ho1
| 2.8085
|
C1Re1
| 61.9783
|
Ca2Pb1
| 1.09935
|
Ba1N2Zr1
| 5.23702
|
Mo2Zr1
| 7.40156
|
Co2Fe1In1
| 9.17116
|
As1Hf1
| 5.63464
|
Er1Pt2Si2
| 2.34323
|
Dy1Hg1
| 2.39767
|
B6Ni21U2
| 1.62178
|
Li4O4Pb1
| 2.23797
|
Au1Er1Ni4
| 3.83805
|
Pd1Yb1
| 3.26732
|
Sn1Zr3
| 3.21006
|
Bi2Ho6Rh1
| 2.03229
|
Pt1Sn1Y1
| 2.83601
|
Re2Sc1
| 4.59076
|
C1Ru3Ta1
| 18.9528
|
Al1Pd1
| 8.60186
|
Ho1In1Zn1
| 3.84946
|
Li1Pb1Pd2
| 0.571128
|
Al2Y3
| 3.19249
|
F3Y1
| 4.35946
|
Ni1Sb2
| 2.97558
|
Al4In3Sr11
| 0.575778
|
Pb1S1
| 2.38034
|
As1Hg1K1
| 1.04089
|
Be2Ti1
| 28.8944
|
Al1Ge1Sr1
| 5.647
|
C1Ni2W4
| 5.40589
|
Ni1Si1Th1
| 3.16182
|
Hf1Pd5
| 3.86585
|
As3Yb4
| 1.09881
|
Al16Hf6Pd7
| 3.28721
|
Ba1P2Ru2
| 9.50025
|
B6Sr1
| 31.7532
|
Te1Zn1
| 2.91044
|
Er1Rh5
| 6.21426
|
Au1C2Cs1
| 3.33356
|
Pd2Si1Tb1
| 1.84059
|
Cd3N2
| 0.66735
|
Au1Ca1In2
| 1.15196
|
Cr2Cu1S4
| 1.89874
|
O6Os2Rb1
| 9.5796
|
Li1Pd2Sn6
| 0.535446
|
Ru3Si2Y1
| 9.33562
|
Hf1Pt1Si1
| 6.18341
|
Ni1Zr1
| 3.18895
|
K1Zn13
| 1.24708
|
Ga1Pd2Sc1
| 2.44487
|
Ge1Y1Zn1
| 4.34264
|
Pb13Rh4Sr3
| 0.325357
|
Ir3Th7
| 1.34903
|
Au1Rb1
| 0.662629
|
O6Pt3Zn1
| 3.12374
|
Ge2Sc1
| 6.85761
|
As1Ga1
| 7.99971
|
Au1Dy1Pb1
| 2.63476
|
Tc1Ti1
| 16.6499
|
Nb4O5
| 8.71805
|
H2Sr1
| 2.19214
|
As2Cu1Y1
| 5.05027
|
Al1F4Na1
| 5.16721
|
C2Dy1Ni1
| 21.1463
|
N1Os1
| 15.2873
|
In1Ni2Zr1
| 4.31515
|
Se4Y2Zn1
| 1.73587
|
Ce1Rh3
| 8.04648
|
N1Re2
| 19.8108
|
In1Pt1Y1
| 1.67478
|
Sr1Tl2
| 0.697961
|
Cu4O3
| 0.710014
|
Ba1O7U2
| 4.72901
|
Re1Si1Ti1
| 14.4911
|
Ti1Zn3
| 3.75237
|
Pt1Sn2
| 1.41834
|
Cd1Pd1
| 3.31255
|
Cd2Sr1
| 0.715945
|
As1Pu1
| 1.42466
|
Au1Si1Y1
| 3.51289
|
Mn1Pd1Te1
| 1.57073
|
Ba1Li1Sb1
| 2.73232
|
End of preview. Expand
in Data Studio
Benchmark AFLOW Data Sets for Machine Learning (Thermal conductivity)
Dataset containing 4887 thermal conductivity values
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/s4qn-b840
- 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 conductivity | W/m-K |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/s4qn-b840")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("Dataset_thermalcond_aflow")
Citation
@misc{https://doi.org/10.18126/s4qn-b840
doi = {10.18126/s4qn-b840}
url = {https://doi.org/10.18126/s4qn-b840}
author = {Clement, Conrad L. and Kauwe, Steven K. and Sparks, Taylor D.}
title = {Benchmark AFLOW Data Sets for Machine Learning (Thermal conductivity)}
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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