Databases¶
FigShare based databases¶
| Database name | Number of data-points | Description |
|---|---|---|
dft_3d |
75993 | Various 3D materials properties in JARVIS-DFT database computed with OptB88vdW and TBmBJ methods |
dft_2d |
1109 | Various 2D materials properties in JARVIS-DFT database computed with OptB88vdW |
dft_3d_2021 |
55723 | Various 3D materials properties in JARVIS-DFT database computed with OptB88vdW and TBmBJ methods |
dft_2d_2021 |
1079 | Various 2D materials properties in JARVIS-DFT database computed with OptB88vdW |
qe_tb |
829574 | Various 3D materials properties in JARVIS-QETB database |
stm |
1132 | 2D materials STM images in JARVIS-STM database |
wtbh_electron |
1440 | 3D and 2D materials Wannier tight-binding Hamiltonian dtaabase for electrons with spin-orbit coupling in JARVIS-WTB (Keyword: 'WANN') |
wtbh_phonon |
15502 | 3D and 2D materials Wannier tight-binding Hamiltonian for phonons at Gamma with finite difference (Keyword:FD-ELAST) |
jff |
2538 | Various 3D materials properties in JARVIS-FF database computed with several force-fields |
alignn_ff_db |
307113 | Energy per atom, forces and stresses for ALIGNN-FF trainig for 75k materials. |
edos_pdos |
48469 | Normalized electron and phonon density of states with interpolated values and fixed number of bins |
megnet |
69239 | Formation energy and bandgaps of 3D materials properties in Materials project database as on 2018, used in megnet |
mp_3d_2020 |
127k | CFID descriptors for materials project |
mp_3d |
84k | CFID descriptors for 84k materials project |
megnet2 |
133k | 133k materials and their formation energy in MP |
twod_matpd |
6351 | Formation energy and bandgaps of 2D materials properties in 2DMatPedia database |
c2db |
3514 | Various properties in C2DB database |
polymer_genome |
1073 | Electronic bandgap and diecltric constants of crystall ine polymer in polymer genome database |
qm9_std_jctc |
130829 | Various properties of molecules in QM9 database |
qm9_dgl |
130829 | Various properties of molecules in QM9 dgl database |
cod |
431778 | Atomic structures from crystallographic open database |
oqmd_3d_no_cfid |
817636 | Formation energies and bandgaps of 3D materials from OQMD database |
oqmd_3d |
460k | CFID descriptors for 460k materials in OQMD |
omdb |
12500 | Bandgaps for organic polymers in OMDB database |
hopv |
4855 | Various properties of molecules in HOPV15 dataset |
pdbbind |
11189 | Bio-molecular complexes database from PDBBind v2015 |
pdbbind_core |
195 | Bio-molecular complexes database from PDBBind core |
qmof |
20425 | Bandgaps and total energies of metal organic frameowrks in QMOF database |
hmof |
137651 | Hypothetical MOF database |
snumat |
10481 | Bandgaps with hybrid functional |
arxiv_summary |
137927 | arXiv summary dataset |
arXiv |
1796911 | arXiv dataset 1.8 million title, abstract and id dataset |
ssub |
1726 | SSUB formation energy for chemical formula dataset |
mlearn |
1730 | Machine learning force-field for elements datasets |
ocp10k |
59886 | Open Catalyst 10000 training, rest validation and test dataset |
ocp100k |
149886 | Open Catalyst 100000 training, rest validation and test dataset |
ocp_all |
510214 | Open Catalyst 460328 training, rest validation and test dataset |
tinnet_N |
329 | TinNet Nitrogen catalyst dataset |
tinnet_O |
747 | TinNet Oxygen catalyst dataset |
tinnet_OH |
748 | TinNet OH group catalyst dataset |
AGRA_O |
1000 | AGRA Oxygen catalyst dataset |
AGRA_OH |
875 | AGRA OH catalyst dataset |
AGRA_CO |
193 | AGRA CO catalyst dataset |
AGRA_CHO |
214 | AGRA CHO catalyst dataset |
AGRA_COOH |
280 | AGRA COOH catalyst dataset |
supercon_3d |
1058 | 3D superconductor DFT dataset |
supercon_2d |
161 | 2D superconductor DFT dataset |
supercon_chem |
16414 | Superconductor chemical formula dataset |
vacancydb |
464 | Vacancy formation energy dataset |
cfid_3d |
55723 | Various 3D materials properties in JARVIS-DFT database computed with OptB88vdW and TBmBJ methods with CFID |
raw_files |
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!