Databases¶
Database name |
Number of data-points |
Description |
---|---|---|
|
75993 |
Various 3D materials properties in JARVIS-DFT database computed with OptB88vdW and TBmBJ methods |
|
1109 |
Various 2D materials properties in JARVIS-DFT database computed with OptB88vdW |
|
55723 |
Various 3D materials properties in JARVIS-DFT database computed with OptB88vdW and TBmBJ methods |
|
1079 |
Various 2D materials properties in JARVIS-DFT database computed with OptB88vdW |
|
829574 |
Various 3D materials properties in JARVIS-QETB database |
|
1132 |
2D materials STM images in JARVIS-STM database |
|
1440 |
3D and 2D materials Wannier tight-binding Hamiltonian dtaabase for electrons with spin-orbit coupling in JARVIS-WTB (Keyword: ‘WANN’) |
|
15502 |
3D and 2D materials Wannier tight-binding Hamiltonian for phonons at Gamma with finite difference (Keyword:FD-ELAST) |
|
2538 |
Various 3D materials properties in JARVIS-FF database computed with several force-fields |
|
307113 |
Energy per atom, forces and stresses for ALIGNN-FF trainig for 75k materials. |
|
48469 |
Normalized electron and phonon density of states with interpolated values and fixed number of bins |
|
69239 |
Formation energy and bandgaps of 3D materials properties in Materials project database as on 2018, used in megnet |
|
127k |
CFID descriptors for materials project |
|
84k |
CFID descriptors for 84k materials project |
|
133k |
133k materials and their formation energy in MP |
|
6351 |
Formation energy and bandgaps of 2D materials properties in 2DMatPedia database |
|
3514 |
Various properties in C2DB database |
|
1073 |
Electronic bandgap and diecltric constants of crystall ine polymer in polymer genome database |
|
130829 |
Various properties of molecules in QM9 database |
|
130829 |
Various properties of molecules in QM9 dgl database |
|
431778 |
Atomic structures from crystallographic open database |
|
817636 |
Formation energies and bandgaps of 3D materials from OQMD database |
|
460k |
CFID descriptors for 460k materials in OQMD |
|
12500 |
Bandgaps for organic polymers in OMDB database |
|
4855 |
Various properties of molecules in HOPV15 dataset |
|
11189 |
Bio-molecular complexes database from PDBBind v2015 |
|
195 |
Bio-molecular complexes database from PDBBind core |
|
20425 |
Bandgaps and total energies of metal organic frameowrks in QMOF database |
|
137651 |
Hypothetical MOF database |
|
10481 |
Bandgaps with hybrid functional |
|
12500 |
arXiv dataset 1.8 million title, abstract and id dataset |
|
1726 |
SSUB formation energy for chemical formula dataset |
|
1730 |
Machine learning force-field for elements datasets |
|
59886 |
Open Catalyst 10000 training, rest validation and test dataset |
|
149886 |
Open Catalyst 100000 training, rest validation and test dataset |
|
510214 |
Open Catalyst 460328 training, rest validation and test dataset |
|
329 |
TinNet Nitrogen catalyst dataset |
|
747 |
TinNet Oxygen catalyst dataset |
|
748 |
TinNet OH group catalyst dataset |
|
1000 |
AGRA Oxygen catalyst dataset |
|
875 |
AGRA OH catalyst dataset |
|
193 |
AGRA CO catalyst dataset |
|
214 |
AGRA CHO catalyst dataset |
|
280 |
AGRA COOH catalyst dataset |
|
1058 |
3D superconductor DFT dataset |
|
161 |
2D superconductor DFT dataset |
|
16414 |
Superconductor chemical formula dataset |
|
464 |
Vacancy formation energy dataset |
|
55723 |
Various 3D materials properties in JARVIS-DFT database computed with OptB88vdW and TBmBJ methods with CFID |
|
144895 |
Figshare links to download raw calculations VASP files from JARVIS-DFT |
All these datasets can be obtained using jarvis-tools as follows, exception to stm
, wtbh_electron
, wtbh_phonon
which have their own modules in jarvis.db.figshare
:
from jarvis.db.figshare import data
d = data('dft_3d') #choose a name of dataset from above
# See available keys
print (d[0].keys())
# Dataset size
print(len(d))
# Visualize an atoms object
from jarvis.core.atoms import Atoms
a = Atoms.from_dict(d[0]['atoms'])
#You can visualize this in VESTA or other similar packages
print(a)
# If pandas framework needed
import pandas as pd
df = pd.DataFrame(d)
print(df)
JARVIS-DFT¶
Description coming soon!