Datasets:
name
stringlengths 1
42
| Tc
float64 0
143
|
|---|---|
Ba0.4K0.6Fe2As2
| 31.2
|
Ca0.4Ba1.25La1.25Cu3O6.98
| 40.1
|
Mo0.39Ru0.61
| 6.9
|
Tm4Os6Sn19
| 1.1
|
Nd1Bi0.99Pb0.01S2F0.3O0.7
| 4.85
|
V0.784Ga0.172Al0.044
| 9.4
|
La1.71Sr0.29Cu0.94Co0.06O4
| 33
|
Re0.1W0.9
| 0
|
N0.82V1
| 2.9
|
Be0.86Pb0.14
| 9.7
|
Nb3Sn0.85Tl0.15
| 18.2
|
Pb0.5Cu0.5Sr0.9La1.1Cu1O5.16
| 28.1
|
Y1Ba2Cu2O6.92
| 0
|
Y1Sr2Cu2.6Al0.4O6.5
| 0
|
Nd1.1Ba1.9Cu3O6.96
| 87.6
|
Y0.887Ba1.962Ca0.15Cu4O8
| 91.8
|
Ba0.33Hf1N1Cl1
| 20.2
|
La2Cu0.975V0.025O4.032
| 40
|
Sm12Cu2.96Ni0.04O6.88
| 83
|
Cu1.5Mo4.5S6
| 10.9
|
La0.9974Tb0.0026
| 5.28
|
Y0.9Ce0.1Ni2B2C1
| 11.9
|
Ga1C1Ni3
| 0
|
La2.9628Gd0.0372In1
| 5.04
|
Pd2Zr0.9Nb0.1Al1
| 2.1
|
Hf0.13Nb0.74Zr0.13
| 0
|
Ru0.8Sr2Y1Cu2.2O8.1
| 65
|
Y1Ba2Cu2.76Co0.24O6.999
| 50.7
|
La2.9553Gd0.0447In1
| 4.3
|
Nb2.5Sn1V0.5
| 14.2
|
La1.73Sr0.27Cu0.94Mn0.06O4
| 37
|
Ho0.75Y0.25Ni2B2C1
| 8.9
|
Ga0.25Fe0.75Se0.85
| 6.8
|
Os0.29V0.71
| 0
|
Zr54.6Ti23.4Ni22
| 2.78
|
Pb2Sr2Sm1Cu3O8
| 0
|
Sr0.7Ba1.3Y1Cu2.8Al0.2O7.11
| 20
|
Ca1Hg3
| 1.6
|
Bi1Ga1Sr1.8Ca1.2Cu2O8
| 24.8
|
Eu2.17Ce0.5Sr1.33Cu2.75Co0.25O9.02
| 0
|
Rb2Mo6Se6
| 0
|
Y0.35Pr0.65Ba1Sr1Cu3O6.9
| 20.3
|
Pr1Ba2Cu3O6.05
| 0
|
Gd0.2Nd1.65Ce0.15Cu1O4
| 18.5
|
La1Mg1
| 0
|
Nb1C0.2N0.74
| 17.5
|
Pr1Os3.8Ru0.2Sb12
| 1.655
|
Bi2Sr1.6Pr0.4Cu1O6.313
| 21.8
|
Bi4Sr3Ca2.7Y0.3Cu4OY
| 81.7
|
Mo0.8W0.2C1
| 12
|
Ga0.02Sn0.98
| 3.726
|
Fe0.5Ba1Sr1Nd1Cu2.5O7.499
| 0
|
Er0.95Gd0.05Rh4B4
| 7.61
|
Au0.499Ga0.501
| 1.17
|
In0.17Tl0.83
| 3.45
|
La1.78Sr0.22Cu0.98Zn0.02O4
| 9.555
|
K0.82Fe1.63Se2
| 32
|
Be22Ru1
| 0
|
La1.82Nd0.03Sr0.15Cu1O4
| 34.2
|
La1.887Ca0.1125Cu1O4
| 20.1
|
C1Nb0.1Ti0.9
| 0
|
La2Cu0.6Li0.4O4
| 0
|
Ba1Fe1.83Zn0.06Co0.11As2
| 17
|
Al0.08Au0.92
| 0.088
|
Fe0.5Ba1Sr1Tm1Cu2.5O7.432
| 40
|
Np1Al4
| 0
|
La1.825Ba0.175Cu1O4
| 26.2
|
Ni1Tb3
| 0
|
La1.5Er0.5Ba2Ca1Cu5O11.907
| 78
|
Sm0.96La0.84Sr0.2Cu1O4
| 35
|
Cu0.5P0.5Sr2Ca1.8Y0.2Cu3O9.55
| 80
|
As2Ti1
| 0
|
Sr1Ga0.5Si1.5
| 3.5
|
Ca0.4La1.25Ba1.35Cu3O7.135
| 76
|
Eu1Fe2As1.89P0.11
| 0
|
Nb0.995Ta0.005
| 9.226
|
Fe1Se0.5S0.5
| 2.75
|
Ce1Rh1In4.84Sn0.16
| 1.2
|
Y0.8Ca0.2Ba2Cu2.8Co0.2O6.98
| 82.5
|
Na0.32Co1H2.6O3.3
| 2.825
|
Er1Rh1Si1
| 0
|
Ta2V1
| 0
|
Bi2Sr2.4Ca0.6Cu2O8
| 94.2
|
La1.751Ba0.249Cu1O4
| 13.7
|
La0.5Y0.5Fe1As1O0.6
| 39.3
|
La2Pt3Ge5
| 8.1
|
Al0.75Ge0.25Nb3.8
| 20.34
|
La1.7Er0.3Ba2Ca0.6Cu4.6O10.92
| 70
|
La1Fe0.99975Mn0.00025As1F0.11O0.89
| 23.8
|
Y1Ba2Cu2.97Zn0.03O6.63
| 42.5
|
Ba0.635K0.365Bi1O3
| 30.4
|
La1.85Sr0.016Ca0.074Ba0.06Cu1O4.01
| 25.4
|
Sm1Fe0.8Zn0.2As1F0.2O0.8
| 0
|
Ga0.1Fe0.9Se0.85
| 6.8
|
Nb0.64Ta0.36
| 6.8
|
La0.97Nd0.03Pt1Si1
| 3.27
|
Bi0.055Sn0.945
| 3.94
|
Pa1
| 0.61
|
Y1Ni1.9Mn0.1B2C1
| 12.7
|
Tl1Sr2Ca1Cu2O6.88
| 0
|
End of preview. Expand
in Data Studio
Machine learning modeling of superconducting critical temperature
Dataset containing experimentally measured superconducting critical temperatures for 16414 materials
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/xlfr-hjrn
- Year: 2022
- Authors: Stanev, Valentin, Oses, Corey, Kusne, A. Gilad, Rodriguez, Efrain, Paglione, Johnpierre, Curtarolo, Stefano, Takeuchi, Ichiro
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| name | input | Material composition | |
| Tc | target | Experimental superconducting critical 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/xlfr-hjrn")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("superconductivity_v1.1")
Citation
@misc{https://doi.org/10.18126/xlfr-hjrn
doi = {10.18126/xlfr-hjrn}
url = {https://doi.org/10.18126/xlfr-hjrn}
author = {Stanev, Valentin and Oses, Corey and Kusne, A. Gilad and Rodriguez, Efrain and Paglione, Johnpierre and Curtarolo, Stefano and Takeuchi, Ichiro}
title = {Machine learning modeling of superconducting critical temperature}
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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