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Material Composition
stringlengths
7
18
EnergyAboveHull
float64
0
957
Formation_energy
float64
-3.21
-0.49
Ba1Sr7V8O24
29.747707
-2.113335
Ba2Bi2Pr4Co8O24
106.702335
-1.311863
Ba2Ca6Fe8O24
171.608093
-1.435607
Ba2Cd2Pr4Ni8O24
284.89819
-0.868639
Ba2Dy6Fe8O24
270.007913
-1.746806
Ba2Gd6Fe8O24
70.973422
-1.903262
Ba2Ho6Fe8O24
291.384616
-1.730647
Ba2La6Co8O24
59.634989
-1.663538
Ba2La6Cr8O24
16.66841
-2.244336
Ba2La6Fe8O24
0
-2.003513
Ba2La6Ga8O24
94.50621
-2.142007
Ba2La6Mn8O24
43.363721
-2.058584
Ba2La6Ni8O24
162.308582
-1.335043
Ba2La6Sc8O24
14.094444
-3.002094
Ba2La6Ti8O24
14.810052
-2.830398
Ba2La6V8O24
53.852034
-2.352157
Ba2Mg6Fe8O24
515.805331
-1.014907
Ba2Nd6Fe8O24
132.814981
-1.819951
Ba2Pr6Co8O24
62.49405
-1.607424
Ba2Pr6Cr8O24
33.654984
-2.125968
Ba2Pr6Fe8O24
58.88532
-1.881721
Ba2Pr6Ga8O24
155.465078
-2.02267
Ba2Pr6Mn8O24
51.309016
-2.003142
Ba2Pr6Ni8O24
162.892176
-1.281904
Ba2Pr6Sc8O24
65.984963
-2.896168
Ba2Pr6Ti8O24
10.174512
-2.755495
Ba2Pr6V8O24
18.973503
-2.282408
Ba2Sm6Fe8O24
178.235739
-1.79498
Ba2Sn2Pr4Ni8O24
276.340269
-1.033511
Ba2Sr6Co8O24
106.745759
-1.210713
Ba2Sr6Fe4Co4O24
109.488956
-1.340598
Ba2Sr6Fe4Ni4O24
183.271284
-1.143709
Ba2Sr6Fe6Co2O24
105.72218
-1.410679
Ba2Sr6Fe6Ni2O24
135.95446
-1.314627
Ba2Sr6Fe7Co1O24
113.060644
-1.433254
Ba2Sr6Fe7Ni1O24
110.285836
-1.399374
Ba2Sr6Fe8O24
89.502961
-1.479235
Ba2Sr6Mn1Fe7O24
102.77542
-1.491472
Ba2Sr6Mn2Fe6O24
96.036038
-1.519665
Ba2Sr6Mn4Fe4O24
86.699751
-1.563023
Ba2Sr6Mn8O24
113.886204
-1.597025
Ba2Sr6Ni8O24
258.828051
-0.801693
Ba2Sr6V8O24
42.133507
-2.100668
Ba2Y6Co8O24
113.483054
-1.643965
Ba2Y6Cr8O24
101.755733
-2.163964
Ba2Y6Fe8O24
84.728965
-1.913357
Ba2Y6Ga8O24
184.387405
-2.066767
Ba2Y6Mn8O24
91.105988
-2.029611
Ba2Y6Ni8O24
152.570887
-1.381147
Ba2Y6Sc8O24
94.602562
-2.939091
Ba2Y6Ti8O24
66.501249
-2.781746
Ba2Y6V8O24
33.004801
-2.369634
Ba3Bi1Pr4Co8O24
109.033411
-1.372961
Ba3Bi4La1Fe8O24
171.956703
-1.18079
Ba3Cd1Pr4Ni8O24
257.075963
-0.967393
Ba3Cd4La1Fe8O24
258.66531
-1.00943
Ba3Sn1Pr4Ni8O24
263.839575
-1.047629
Ba3Sn4La1Fe8O24
285.948164
-1.230693
Ba3Sr3La2Mn1Fe7O24
0
-2.224517
Ba4Ba4Mn2Fe6O24
271.906598
-0.93127
Ba4Bi4Fe8O24
187.391046
-1.093094
Ba4Bi4Zr7Fe1O24
138.892855
-2.193333
Ba4Ca4Co4Ni4O24
296.095913
-0.941304
Ba4Ca4Co6Ni2O24
247.684975
-1.03977
Ba4Ca4Co7Ni1O24
212.756631
-1.099726
Ba4Ca4Fe1Co7O24
187.894409
-1.181994
Ba4Ca4Fe1Ni7O24
332.212137
-0.817964
Ba4Ca4Fe2Co6O24
205.56403
-1.196703
Ba4Ca4Fe2Ni6O24
317.356871
-0.902577
Ba4Ca4Fe8O24
163.185749
-1.413634
Ba4Ca4Mn1Co7O24
180.660958
-1.206799
Ba4Ca4Mn1Ni7O24
349.605405
-0.823979
Ba4Ca4Mn2Co6O24
197.12413
-1.240287
Ba4Ca4Mn2Ni6O24
346.087908
-0.920662
Ba4Ca4Mn4Co4O24
195.672754
-1.340437
Ba4Ca4Mn4Ni4O24
313.722881
-1.122277
Ba4Ca4Mn6Co2O24
198.592674
-1.432124
Ba4Ca4Mn6Fe2O24
192.010418
-1.503463
Ba4Ca4Mn6Ni2O24
234.968862
-1.345693
Ba4Ca4Mn7Co1O24
206.610134
-1.471411
Ba4Ca4Mn7Fe1O24
192.712755
-1.517686
Ba4Ca4Mn7Ni1O24
213.333228
-1.43966
Ba4Ca4Ni4Co4O24
330.939913
-0.90646
Ba4Ca4Ni6Co2O24
364.707101
-0.822638
Ba4Ca4Ni7Co1O24
354.737752
-0.779144
Ba4Cd4Fe8O24
278.984665
-0.909699
Ba4Cd4Hf1Zr6Fe1O24
98.580755
-2.235525
Ba4Cd4Zr7Fe1O24
151.553671
-2.16402
Ba4Ce4Mn8O24
119.114168
-1.936351
Ba4Dy4Fe8O24
236.177315
-1.617726
Ba4Dy4Mn8O24
137.408713
-1.853919
Ba4Er4Mn8O24
150.999876
-1.85051
Ba4Gd4Co8O24
130.597392
-1.453169
Ba4Gd4Fe8O24
108.666855
-1.71441
Ba4Gd4Mn8O24
105.693165
-1.838974
Ba4Ho4Fe8O24
254.265051
-1.605109
Ba4Ho4Mn8O24
144.194572
-1.852193
Ba4La1Mn8Zn3O24
244.558934
-1.296834
Ba4La1Zn3Co8O24
259.721126
-0.920564
Ba4La1Zn3Fe8O24
253.584855
-1.157167
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Predicting the thermodynamic stability of perovskite oxides using machine learning models

Dataset containing DFT-calculated stabilities (as convex hull energies) of 1929 perovskite oxides

Dataset Information

Fields

Field Role Description Units
Material Composition input Perovskite composition
EnergyAboveHull target DFT-calculated convex hull energy as meV/atom meV/atom
Formation_energy target DFT-calculated formation energy relative to pure e 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/qe5y-2dnz")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("perovskite_stability_v1.1")

Citation

@misc{https://doi.org/10.18126/qe5y-2dnz
doi = {10.18126/qe5y-2dnz}
url = {https://doi.org/10.18126/qe5y-2dnz}
author = {Li, Wei and Jacobs, Ryan and Morgan, Dane}
title = {Predicting the thermodynamic stability of perovskite oxides using machine learning models}
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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