formula
stringlengths 4
12
| target
float64 -0.64
4.24
|
|---|---|
Bi1F1Hf1O2
| 0.62
|
F1Li1N1O1Y1
| 1.48
|
As1N1O2Sc1
| 1.2
|
O3Pb1Ru1
| 0.78
|
Au1Ga1O2S1
| 1.24
|
Cs1F1Hg1N1O1
| 1.76
|
N1O2Os1W1
| 1.5
|
Hg1O3Te1
| 1.06
|
F1N1O1Sr1V1
| 0.46
|
Au1F1K1N1O1
| 1.76
|
Be1F1N1Na1O1
| 1.68
|
Co1K1O3
| 0.82
|
N1O2Pt1Sn1
| 1.16
|
Fe1O3Ta1
| 0.32
|
N3Sc1W1
| 0.6
|
Mo1O3V1
| 0.66
|
N1O2V1Zr1
| 0.62
|
Au1F1N1O1Si1
| 1.3
|
F1Ga1Mo1N1O1
| 0.58
|
Bi2N2O1
| 1.18
|
F1N1O1Sn1Tl1
| 0.76
|
O2S1W1Zn1
| 0.72
|
N3Re1Sr1
| 0.68
|
Cs1F1N1O1Ti1
| 0.7
|
Hf1La1O2S1
| 0.56
|
As1O2Pd1S1
| 0.92
|
Ge1N3Nb1
| 0.98
|
Cr1N1O2Pd1
| 0.84
|
F1O2Rb1Sn1
| 0.36
|
Mo1N1O2W1
| 0.64
|
Co1N2O1Pd1
| 1.44
|
N1O2Rb1W1
| 0
|
Cs1F1Ir1O2
| 1.24
|
Cs1N3W1
| 1.36
|
B1Be1F1N1O1
| 1.54
|
As1F1N1O1Zr1
| 0.86
|
Be1N3Sc1
| 1.48
|
Cu1Fe1N1O2
| 1.1
|
In1Mo1N3
| 0.82
|
Mn1N1O2W1
| 0.42
|
F1N1Nb1O1V1
| 1.06
|
N1Na1O2Tl1
| 1.6
|
Cu1Ge1N1O2
| 0.96
|
B1Li1N2O1
| 2.22
|
Be1N3Sb1
| 1.9
|
O3Te1V1
| 0.62
|
Be1Fe1N3
| 1.32
|
F1O2Te1Zr1
| 0.76
|
Mg1N3Os1
| 1.44
|
Ir1O2S1Ta1
| 0.56
|
F1N1Na1O1Ru1
| 1.08
|
N3Ni2
| 1.48
|
N3Nb1Re1
| 1.02
|
O3Re1Tl1
| 0.42
|
Cr1O2S1Sc1
| 0.62
|
Au1Mg1N1O2
| 1.7
|
Ca1N2Na1O1
| 2.8
|
Be1F1N1O1Si1
| 1.54
|
Mn1N1O2Te1
| 1
|
Ge1O3Pd1
| 1.04
|
Cu1Ni1O2S1
| 1.06
|
As1La1N2O1
| 0.7
|
Al1N1O2Sb1
| 0.94
|
Hf1N2O1Pd1
| 1
|
Ge1N2O1Sn1
| 1.1
|
F1N1O1Pb1W1
| 0.54
|
F1N1O1Rb1Tl1
| 1.38
|
O2S1Sn1Ti1
| 0.48
|
As1N3Ru1
| 1.78
|
Na1O2S1Y1
| 1.02
|
F1Ga1N1O1V1
| 0.56
|
N1O2Sn1V1
| 0.44
|
Li1N3Rh1
| 2.02
|
Fe1N2O1Sn1
| 1.1
|
N2O1Pt1Y1
| 1.36
|
Bi1Hg1N3
| 2.22
|
Au1F1O2V1
| 0.66
|
Be1F1O2Pt1
| 1.64
|
Ca2N3
| 2.48
|
N3Rb1Y1
| 2.16
|
N1O2Pd1Rh1
| 1.78
|
N2O1Ta1Zn1
| 0.64
|
Cu1O2Rh1S1
| 1.1
|
Ir1N1O2Zn1
| 1.44
|
Ag1F1O2Ti1
| -0.04
|
Bi1Ni1O3
| 0.72
|
Ga1N3Re1
| 0.9
|
F1Mn1O2Ti1
| 0.44
|
N2O1Rb1Rh1
| 2.02
|
N1O2Rb1Ru1
| 0.88
|
F1O2Si2
| 1.36
|
F1N1Nb1O1Pd1
| 0.66
|
Au1N1Ni1O2
| 1.7
|
Mg1N1O2Y1
| 1.06
|
F1N1O1Re1Zn1
| 0.9
|
Cd1N3Tl1
| 2.32
|
Ag1Cu1N1O2
| 1.88
|
Ca1F1O2Si1
| 0.7
|
N2O1Os1Tl1
| 1.18
|
Ag1O3Os1
| 1.1
|
End of preview. Expand
in Data Studio
Computational screening of perovskite metal oxides for optimal solar light capture
Dataset containing 9646 perovskite formation energy data points
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/xmh8-d711
- Year: 2011
- Authors: Castelli, Ivano E., Olsen, Thomas, Datta, Soumendu, Landis, David D., Dahl, Søren, Thygesena, Kristian S., Jacobsen, Karsten W.
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| formula | input | Material composition | |
| target | target | Formation energy | eV/atom |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/xmh8-d711")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("Dataset_perovskite_formationE")
Citation
@misc{https://doi.org/10.18126/xmh8-d711
doi = {10.18126/xmh8-d711}
url = {https://doi.org/10.18126/xmh8-d711}
author = {Castelli, Ivano E. and Olsen, Thomas and Datta, Soumendu and Landis, David D. and Dahl, Søren and Thygesena, Kristian S. and Jacobsen, Karsten W.}
title = {Computational screening of perovskite metal oxides for optimal solar light capture}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2011}}
License
other
This dataset was exported from Foundry-ML, a platform for materials science datasets.
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