NIST-JARVIS

Welcome to NIST-JARVIS!

JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials design using classical force-field, density functional theory, machine learning calculations and experiments.

NIST-JARVIS Resources

Resource Summary Website
Homepage Description and API Link
DFT Density functional theory data Link
FF Evaluation of classical force field Link
ML Machine learning models Link
Tools Scripts for running simulations Link
Downloads Downloadable metadata Link
Notebooks Jupyter/Google-Colab notebooks Link
Heterostructure 2D heterostructure properties Link
WannierTB Wannier tight binding models Link
BeyondDFT High-level ab-initio methods Link
Publications JARVIS-related publications Link
Tools docs Documentation (docs) of tools Link
DB docs Documentation on the database Link
Tools pypi Pypi repository of tools Link
Workshops JARVIS-related workshops Link
ResearchG. Social media researchgate page. Link
Twitter Social media twitter page Link
Facebook Social media facebook page Link
Linkedin Social media linkedin page Link
SlideShare Collection of presentation slides Link
YouTube Social media youtube page Link
Google-group Social media google-group Link

JARVIS-DFT

JARVIS-DFT is a density functional theory based database for ~40000 3D, ~1000 2D materials and around a million calculated properties. JARVIS-DFT mainly uses vdW-DF-OptB88 functional for geometry optimization. It also uses beyond-GGA approaches, including Tran-Blaha modified Becke-Johnson (TBmBJ) meta-GGA, PBE0, HSE06, DMFT, G0W0 for analyzing selective cases. In addition to hosting conventional properties such as formation energies, bandgaps, elastic constants, piezoelectric constants, dielectric constants, and magnetic moments, it also contains unique datasets, such as exfoliation energies for van der Waals bonded materials, spin-orbit coupling spillage, improved meta-GGA bandgaps, frequency-dependent dielectric function, spin-orbit spillage, spectroscopy limited maximum efficiency (SLME), infrared (IR) intensities, electric field gradient (EFG), heterojunction classifications, and Wannier tight-binding Hamiltonians. These datasets are compared to experimental results wherever possible, to evaluate their accuracy as predictive tools. JARVIS-DFT introduces protocols such as automatic k-point convergence that can be critical for obtaining precise and accurate calculation results.

Materials class Materials
3D-bulk 33482
2D-bulk 2293
1D-bulk 235
1D-bulk 235
0D-bulk 413
2D-single layer 1105
2D-double layer 102
Molecules 12
Heterostructure 3
Total 37646

Functionals Numbers
vdW-DF-OptB88 (OPT) 37646
vdW-DF-OptB86b (MK) 109
vdW-DF-OptPBE (OR) 111
PBE 99
LDA 92

Properties Numbers
Optimized crystal-structure (OPT) 37646
Formation-energy (OPT) 37646
Bandgap (OPT) 37646
Exfoliation energy (OPT) 819
Bandgap (TBmBJ) 15655
Bandgap (HSE06) 40
Bandgap (PBE0) 40
Frequency dependent dielectric tensor (OPT) 34045
Frequency dependent dielectric tensor (TBmBJ) 15655
Theoretical solar-cell efficiency (SLME) (TBmBJ) 5097
Topological spin-orbit spillage (PBE+SOC) 11500
Elastic-constants (OPT) 15500
Finite-difference phonons at Г-point (OPT) 15500
Work-function, electron-affinity (OPT) 1105
Wannier tight-binding Hamiltonians(PBE+SOC) 1771
Seebeck coefficient (OPT, BoltzTrap) 22190
Power factor (OPT, BoltzTrap) 22190
Effective mass (OPT, BoltzTrap) 22190
Magnetic moment (OPT) 37528
Piezoelectric constant (OPT, DFPT) 5015
Dielectric tensor (OPT, DFPT) 5015
Dielectric tensor (OPT, DFPT) 5015
Infrared intensity (OPT, DFPT) 5015
DFPT phonons at Г-point (OPT) 5015
NQR-Electric field gradient (OPT) 5015
Non-resonant Raman intensity (OPT, DFPT) 250
Scanning tunneling microscopy images (PBE+SOC) 770

JARVIS-FF

JARVIS-FF is a repository of classical force-field/potential calculation data intended to help users select the most appropriate force-field for a specific application. Many classical force-fields are developed for a particular set of properties (such as energies), and may not have been tested for properties not included in training (such as elastic constants, or defect formation energies). JARVIS-FF provides an automatic framework to consistently calculate and compare basic properties, such as the bulk modulus, defect formation energies, phonons, etc. that may be critical for specific molecular-dynamics simulations. JARVIS-FF relies on DFT and experimental data to evaluate accuracy.

Force-fields Numbers
EAM 92
Tersoff 9
ReaxFF 5
COMB 6
AIREBO 2
MEAM 1
EIM 1

JARVIS-ML

JARVIS-ML is a repository of machine learning (ML) model parameters, descriptors, and ML-related input and target data. JARVIS-ML introduced Classical Force-field Inspired Descriptors (CFID) as a universal framework to represent a material’s chemistry-structure-charge related data. With the help of CFID and JARVIS-DFT data, several high-accuracy classifications and regression ML models were developed, with applications to fast materials-screening and energy-landscape mapping. Some of the trained property models include formation energies, exfoliation energies, bandgaps, magnetic moments, refractive index, dielectric constants, thermoelectric performance, and maximum piezoelectric and infrared modes. Also, several ML interpretability analyses have provided physical-insights beyond intuitive materials-science knowledge. These models, the workflow, dataset etc. are disseminated to enhance the transparency of the work. Recently, JARVIS-ML expanded to include ML models to analyze STM-images in order to directly accelerate the interpretation of experimental images.

Regression models Training data Mean abs. errors
Formation energy (eV/atom) 24549 0.12
OPT bandgap (eV) 22404 0.32
TBmBJ bandgap (eV) 10499 0.44
Bulk mod., Kv (GPa) 10954 10.5
Formation energy (eV/atom) 24549 0.12
Formation energy (eV/atom) 24549 0.12
Shear mod., Gv (GPa) 10954 9.5
Refr. Index(x) (OPT) 12299 0.54
Refr. Index(x) (TBmBJ) 6628 0.45
IR mode (OPT) (cm-1) 3411 77.84
Born eff. Charge (OPT) 3411 0.60
Plane-wave cutoff (OPT)(eV) 24549 85
Plane-wave cutoff (OPT)(eV) 24549 85
K-point length (OPT)(Angs.) 24549 9.09
2D-Exfoliation energy (OPT) (eV/atom) 616 37.3
Plane-wave cutoff (OPT)(eV) 24549 85

Classification models Training data ROC AUC
Metal/non-metal (OPT) 24549 0.95
Magnetic/Non-magnetic (OPT) 24549 0.96
High/low solar-cell efficiency (TBmBJ) 5097 0.90
High/low piezoelectric coeff 3411 0.86
High/low Dielectric 3411 0.93
High/low n-Seebeck coeff 21899 0.95
High/low n-power factor 21899 0.80
High/low p-Seebeck coeff 21899 0.96
High/low p-power factor 21899 0.82

JARVIS-Heterostructure

JARVIS-Heterostructure provide interface-design tools for 2D materials in the JARVIS-DFT database using Zur algorithm and Anderson rule. Some of the properties available are: work function, band-alignment, and heterostructure classification. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions.

Type Numbers
Type-I (Symmetric) 75744
Type-II (Staggered) 85723
Type-III (Broken) 65312
Total 226779

JARVIS-WannierTB

JARVIS-WannierTB provides Wannier Tight binding Hamiltonians and an interface to solve these Hamiltonians for arbitrary k-points on-the-fly. We evaluate the accuracy of the WTBHs by comparing the Wannier band structures to directly calculated DFT band structures on both the set of k-points used in the Wannierization as well as independent k-points from high symmetry lines.

Materials Numbers
3D-bulk 1406
2D-monolayer 365
Total 1771

Founder and developer: Dr. Kamal Choudhary

Contributors: (Drs.) Francesca Tavazza, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agarwa, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe.

Funding support: NIST-MGI


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