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`	12500	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!

JARVIS-Formation energy and bandgap¶

JARVIS-2D Exfoliation energies¶

JARVIS-MetaGGA (dielectric function and SLME, solar cells)¶

JARVIS-STM and STEM¶

JARVIS-WannierTB¶

JARVIS-Elastic constants¶

JARVIS-Topological materials (Spin-orbit Spillage)¶

JARVIS-DFPT (Piezoelectric, IR, Raman, dielectric, BEC)¶

JARVIS-BoltzTrap (Thermoelectrics coeff, eff. mass)¶

JARVIS-Magnetic moments¶

JARVIS-DFPT (Piezoelectric, IR, dielectric)¶

JARVIS-EFG¶

JARVIS-PBE0 and HSE06¶

JARVIS-Heterostructure¶

JARVIS-EDOS-PDOS¶

JARVIS-Kpoint and cut-off¶

JARVIS-FF¶

Energetics¶

Elastic constants¶

Vacancy formation energy¶

Surface energy and Wulff-plots¶

Phonon DOS¶

JARVIS-RAW Files¶

JARVIS-DFT structure relaxation¶

JARVIS-DFT Elastic constants/finite difference¶

JARVIS-WannierTB¶

JARVIS-STM and STEM¶

External datasets used for ML training¶

Materials project dataset¶

QM9 dataset¶

OQMD dataset¶

AFLOW dataset¶

Polymer genome dataset¶

COD dataset¶

OMDB dataset¶

QMOF dataset¶

C2DB dataset¶

HPOV dataset¶