formula
stringlengths 2
15
| target
float64 43.9
2.11k
|
|---|---|
Zr1
| 268.987
|
K2Mg5Sn3
| 194.685
|
C1In1La3
| 248.043
|
Al3B2Ru4
| 536.231
|
Au1Ho1Pb1
| 174.85
|
F2Xe1
| 121.56
|
Dy1S2
| 327.586
|
Nb3Te4
| 252.974
|
Rb2Te1
| 119.013
|
Sb2Tb1
| 209.205
|
Hf1Pd5
| 290.738
|
O3Pb1Zr1
| 417.509
|
Ag1Al1Se2
| 213.309
|
Pd1Sb1Tb1
| 218.792
|
Ga1La1Zn1
| 298.434
|
Ag1As1S1
| 179.214
|
Ca1Pd1
| 239.886
|
Cd2Yb1
| 180.449
|
K1Li1Se1
| 262.784
|
Al2N1Nb3
| 560.437
|
Pt7Sb1
| 168.261
|
Ag1Er1
| 194.442
|
Ca2O4Pb1
| 401.37
|
Cl4K2Pd1
| 177.39
|
P1Ru1Zr1
| 440.648
|
As1Li1Mg1
| 429.918
|
Au1Ga1Zr1
| 280.281
|
In2Li1Rh1
| 261.367
|
Ca3Ge13Ir4
| 304.608
|
Ba1Mg4Si3
| 441.897
|
Mo3P1
| 431.5
|
Ni1Zr2
| 186.771
|
As1Hf1Ru1
| 335.638
|
Co2Ge1Zn1
| 177.644
|
N1Ni2W3
| 437.29
|
B6K1
| 997.505
|
Lu1Pb2
| 107.714
|
Au1Dy1
| 173.744
|
Pt3Sn1
| 216.151
|
Sn1Zr3
| 248.565
|
Bi1
| 70.1777
|
In1P1Pd5
| 293.09
|
In2O3
| 406.181
|
C1Sc3Tl1
| 438.646
|
Al1Pd1Y1
| 267.734
|
Ho1In3
| 203.247
|
Dy1Rh2Si2
| 417.538
|
Al2Y1
| 472.956
|
Pu1Se1
| 198.162
|
Co1Y1
| 201.353
|
Al4Er1Ni1
| 424.062
|
Er5Si3
| 306.544
|
As1Hf1
| 290.455
|
Be2Re1
| 593.266
|
Al1Ge1Sc1
| 423.378
|
C1Pb1Pd3
| 218.246
|
Cu2In1Y1
| 270.653
|
Hf1Pt1
| 162.128
|
As3La4
| 231.622
|
Al16Hf6Pd7
| 403.516
|
Ba1Os2P2
| 274.343
|
B6Os1Y2
| 746.739
|
Ho1In1
| 170.363
|
Er1Ru2Si2
| 390.134
|
Au1Be5
| 630.03
|
Rh2Si2Y1
| 456.646
|
Ce1Ga1Ni1
| 273.716
|
Au1Ca1Cd1
| 169.827
|
Ga12Lu4Pd1
| 316.029
|
O6Os2Rb1
| 438.488
|
Dy1Ga1Pt1
| 189.692
|
Pt1Sb2
| 247.753
|
La1Rh2
| 260.816
|
Er1Ge2Rh2
| 316.732
|
F6K2Mn1
| 217.996
|
Ho2Rh3Sn5
| 228.839
|
Cl6Cs2Te1
| 121.03
|
Ni2Si2Zr1
| 480.417
|
P1Rh2
| 399.457
|
Au1Pb4Rb3
| 91.9333
|
N1Ru2Zr4
| 374.553
|
Ca1O3Ti1
| 749.293
|
As1Fe1Nb1
| 349.755
|
Au1Cu4Tb1
| 157.576
|
Ir5Th1
| 294.768
|
P2Zn3
| 365.797
|
In1Pd2
| 221.119
|
As2Cu1Er1
| 314.693
|
Al1F3
| 730.728
|
C2Ir1
| 424.387
|
Cu4O3
| 266.444
|
Ir2S3Sn3
| 233.51
|
Si136
| 492.719
|
Ce2Pd21Si6
| 267.622
|
O4Sb1V1
| 616.724
|
In1Pt2Tb1
| 200.711
|
Sr1Tl2
| 140.184
|
Ca1O3Rh1
| 523.085
|
Ba1O3Ti1
| 512.805
|
Re1Si1Ti1
| 483.243
|
End of preview. Expand
in Data Studio
Benchmark AFLOW Data Sets for Machine Learning (Debye Temperature)
Dataset containing calculated Debye temperatures of 4896 materials
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/33r4-8t58
- 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 | Debye Temperature | K |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/33r4-8t58")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("Dataset_debyeT_aflow")
Citation
@misc{https://doi.org/10.18126/33r4-8t58
doi = {10.18126/33r4-8t58}
url = {https://doi.org/10.18126/33r4-8t58}
author = {Clement, Conrad L. and Kauwe, Steven K. and Sparks, Taylor D.}
title = {Benchmark AFLOW Data Sets for Machine Learning (Debye Temperature)}
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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