Welcome to NIST-JARVIS infrastructure!
JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments. Find more details https://arxiv.org/abs/2007.01831 .
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 |
| Social media twitter page | Link | |
| Social media facebook page | Link | |
| 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.
