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. The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials. The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons. The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus.

Dataset for 3D materials

Websites: 1) https://jarvis.nist.gov/jarvisdft , 2) https://jarvis-tools.readthedocs.io/en/master/databases.html , 3) https://jarvis-tools.readthedocs.io/en/master/publications.html

Loading the dataset:

unzip jdft_3d-12-12-2022.json.zip import os, json import pandas as pd f = open(' jdft_3d-12-12-2022.json', 'r') data3d=json.load(f) f.close() df=pd.DataFrame(data3d) print (df)

or

pip install jarvis-tools from jarvis.db.figshare import data data2d = data('dft_3d')

For more details about using the dataset, use the jupyter-notebooks: https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks

JARVIS-DFT is a repository of density functional theory based calculation data for materials.

Dataset

JARVIS C2DB

  Description

The JARVIS-C2DB dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configurations from the Computational 2D Database (C2DB), which contains a variety of properties for 2-dimensional materials across more than 30 differentcrystal structures. JARVIS is a set of tools and datasets built to meet current materials design challenges.Additional details stored in dataset… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/JARVIS_C2DB.

Dataset

JARVIS-Polymer-Genome

  Description

The JARVIS-Polymer-Genome dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Polymer Genome dataset, as created for the linked publication (Huan, T., Mannodi-Kanakkithodi, A., Kim, C. et al.). Structures were curated from existing sources and the original authors' works, removing redundant, identical structures before calculations… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/JARVIS-Polymer-Genome.

JARVIS-Superconductor database for 3D & 2D materials

unzip jarvis_epc_data_figshare_1058.json.zip

or unzip jarvis_epc_data_2d.json.zip

import pandas as pd df=pd.read_json('jarvis_epc_data_figshare_1058.json') print (df.columns) Index(['stability', 'jid', 'atoms', 'cfid', 'wlog', 'lamb', 'Tc', 'a2F', 'a2F_original_x', 'a2F_original_y', 'press'], dtype='object') print (df[['jid','Tc']])

Here, jid is JARVIS-DFT ID, atoms is jarvis.core.atoms as dictionary object, wlog; lamb;Tc are omega log, coupling constant and transition temperature using McMillan Allen Dynes formula, a2F is Eliashberg function from 0 to 100 meV.

We perform electron-phonon coupling calculations to establish a large and systematic database of BCS superconducting properties.

Please cite the following if you use these datatsets: 1) https://www.nature.com/articles/s41524-022-00933-1 2) https://doi.org/10.1021/acs.nanolett.2c04420

For any questions/concerns, raise a issue on (https://github.com/usnistgov/jarvis/issues) or write to kamal.choudhary@nist.gov.

Enjoy!

The JARVIS_CFID_OQMD dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Open Quantum Materials Database (OQMD), created to hold information about the electronic structure and stability of organic materials for the purpose of aiding in materials discovery. Calculations were performed at the DFT level of theory, using the PAW-PBE functional implemented by VASP. This dataset also includes classical force-field inspired descriptors (CFID) for each configuration. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

This data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Site Characterization Data: C.Jarvis data set is a product is being provided. During the summers of 1983 and 1984, the National Aeronautics and Space Administration (NASA) conducted an intensive experiment in a portion of the Superior National Forest (SNF) near Ely, Minnesota, USA. The purpose of the experiment was to investigate the ability of remote sensing to provide estimates of biophysical properties of ecosystems, such as leaf area index (LAI), biomass and net primary productivity (NPP). The study area covered a 50 x 50 km area centered at approximately 48 degrees North latitude and 92 degrees West longitude in northeastern Minnesota at the southern edge of the North American boreal forest. The SNF is mostly covered by boreal forest. Boreal forests were chosen for this project because of their relative taxonomic simplicity, their great extent, and their potential sensitivity to climatic change. Satellite, aircraft, helicopter and ground observations were obtained for the study area. These data comprise a unique dataset for the investigation of the relationships between the radiometric and biophysical properties of vegetated canopies. This is perhaps the most complete dataset of its type ever collected over a forested region. A key goal of the experiment was to use the aircraft measurements to scale up to satellite observations for the remote sensing of biophysical parameters.

Cite this dataset

Kirklin, S., Saal, J. E., Meredig, B., Thompson, A., Doak, J. W., Aykol, M., Rühl, S., and Wolverton, C. JARVIS CFID OQMD. ColabFit, 2023. https://doi.org/10.60732/967596c1

  View on the ColabFit Exchange

https://materials.colabfit.org/id/DS_u8strp7hm0cy_0

  Dataset Name

JARVIS CFID OQMD

  Description

The JARVIS_CFID_OQMD dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/JARVIS_CFID_OQMD.

This data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Site Characterization Data: C.Jarvis data set is a product is being provided. During the summers of 1983 and 1984, the National Aeronautics and Space Administration (NASA) conducted an intensive experiment in a portion of the Superior National Forest (SNF) near Ely, Minnesota, USA. The purpose of the experiment was to investigate the ability of remote sensing to provide estimates of biophysical properties of ecosystems, such as leaf area index (LAI), biomass and net primary productivity (NPP). The study area covered a 50 x 50 km area centered at approximately 48 degrees North latitude and 92 degrees West longitude in northeastern Minnesota at the southern edge of the North American boreal forest. The SNF is mostly covered by boreal forest. Boreal forests were chosen for this project because of their relative taxonomic simplicity, their great extent, and their potential sensitivity to climatic change. Satellite, aircraft, helicopter and ground observations were obtained for the study area. These data comprise a unique dataset for the investigation of the relationships between the radiometric and biophysical properties of vegetated canopies. This is perhaps the most complete dataset of its type ever collected over a forested region. A key goal of the experiment was to use the aircraft measurements to scale up to satellite observations for the remote sensing of biophysical parameters.

First reconnaissance at Jarvis Island, Palmyra Atoll, and Kingman Reef, Line Islands, for species diversity, community structure, deep-water habitats, and bottom topography of meso- and subphotic island slopes between 150-1027 m (mostly at 200-800 m) using Hawaii Undersea Research Laboratory (HURL) PISCES research submersibles. Data were collected during July 2005 by Bruce Mundy, Frank Parrish, and James Maragos (USFWS), with major assistance with the staff of the Hawaii Undersea Research Laboratory. Submersible dives were of 6-9 hours each: 1 at Jarvis Island, 2 at Palmyra Atoll, and 3 at Kingman Reef. Five of the dives used survey protocols from previous work in the Northwestern Hawaiian Islands, to allow comparisons with another region (one dive at Kingman Reef was purely exploratory). The protocol consisted of: (1) descent to a depth allowed by local conditions and time constraints; (2) exploratory observations upslope, (3) four 30 minute transects at 500, 450, 400, and 350 m during which observers identified, counted, and estimated the sizes of all fish and invertebrates with the aid of a calibrated laser scale projected on the substrate, and (4) more exploration upslope, if time allowed. Exploratory portions of the dives collected data on the species and habitat parameters observed, but did not include estimates of numbers or sizes of common organisms. Continuous audio and video files from the entire dives were recorded, from which data on biodiversity and habitat structure were extracted in the laboratory. Data analysis by Frank Parrish and Bruce Mundy.

Various properties of 24,759 bulk and 2D materials computed with the OptB88vdW and TBmBJ functionals taken from the JARVIS DFT database. This dataset was modified from the JARVIS ML training set developed by NIST (1-2). The custom descriptors have been removed, the column naming scheme revised, and a composition column created. This leaves the training set as a dataset of composition and structure descriptors mapped to a diverse set of materials properties.Available as Monty Encoder encoded JSON and as the source Monty Encoder encoded JSON file. Recommended access method is with the matminer Python package using the datasets module.Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.Dataset discussed in: Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape Kamal Choudhary, Brian DeCost, and Francesca Tavazza Phys. Rev. Materials 2, 083801Original Data file sourced from:choudhary, kamal (2018): JARVIS-ML-CFID-descriptors and material properties. figshare. Dataset.

This data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Vegetation Cover Data: C. Jarvis Data Set is a product is being provided. During the summers of 1983 and 1984, the National Aeronautics and Space Administration (NASA) conducted an intensive experiment in a portion of the Superior National Forest (SNF) near Ely, Minnesota, USA. The purpose of the experiment was to investigate the ability of remote sensing to provide estimates of biophysical properties of ecosystems, such as leaf area index (LAI), biomass and net primary productivity (NPP). The study area covered a 50 x 50 km area centered at approximately 48 degrees North latitude and 92 degrees West longitude in northeastern Minnesota at the southern edge of the North American boreal forest. The SNF is mostly covered by boreal forest. Boreal forests were chosen for this project because of their relative taxonomic simplicity, their great extent, and their potential sensitivity to climatic change. Satellite, aircraft, helicopter and ground observations were obtained for the study area. These data comprise a unique dataset for the investigation of the relationships between the radiometric and biophysical properties of vegetated canopies. This is perhaps the most complete dataset of its type ever collected over a forested region.

This data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Site Characterization Data: C.Jarvis data set is a product is being provided. During the summers of 1983 and 1984, the National Aeronautics and Space Administration (NASA) conducted an intensive experiment in a portion of the Superior National Forest (SNF) near Ely, Minnesota, USA. The purpose of the experiment was to investigate the ability of remote sensing to provide estimates of biophysical properties of ecosystems, such as leaf area index (LAI), biomass and net primary productivity (NPP). The study area covered a 50 x 50 km area centered at approximately 48 degrees North latitude and 92 degrees West longitude in northeastern Minnesota at the southern edge of the North American boreal forest. The SNF is mostly covered by boreal forest. Boreal forests were chosen for this project because of their relative taxonomic simplicity, their great extent, and their potential sensitivity to climatic change. Satellite, aircraft, helicopter and ground observations were obtained for the study area. These data comprise a unique dataset for the investigation of the relationships between the radiometric and biophysical properties of vegetated canopies. This is perhaps the most complete dataset of its type ever collected over a forested region. A key goal of the experiment was to use the aircraft measurements to scale up to satellite observations for the remote sensing of biophysical parameters.

Site characterization parameters (canopy density, litter components, soil characterization: color, moisture, components) for selected sites within the Superior National Forest, MN during 1988-89

from jarvis.db.jsonutils import loadjson d=loadjson('monolayer_data.json') import pandas as pd df=pd.DataFrame(d) print (df) phi atoms jid 0 {'nelect': 48, 'phi': 4.73414095269429, 'scf_v... {'lattice_mat': [[3.353617811446221, 0.0, 0.0]... JVASP-677 1 {'nelect': 48, 'phi': 4.530152088053274, 'scf_... {'lattice_mat': [[3.4519950338496734, 0.0, 0.0... JVASP-675 2 {'nelect': 48, 'phi': 4.842575064739249, 'scf_... {'lattice_mat': [[3.4778329262343175, 0.0, 0.0... JVASP-676 3 {'nelect': 49, 'phi': 4.152692578895887, 'scf_... {'lattice_mat': [[4.389372163294263, 0.0, 0.0]... JVASP-6835 4 {'nelect': 38, 'phi': 4.8566275698291665, 'scf... {'lattice_mat': [[4.111785578032976, 0.0, 0.0]... JVASP-6838 ... ... ... ... 1100 {'nelect': 66, 'phi': 6.835065568894292, 'scf_... {'lattice_mat': [[5.048591416059411, 0.0, -0.0... JVASP-75420 1101 {'nelect': 114, 'phi': 5.9478129766546095, 'sc... {'lattice_mat': [[3.934471688974004, 0.0, 0.0]... JVASP-75426 1102 {'nelect': 116, 'phi': 5.39584804845677, 'scf_... {'lattice_mat': [[3.9528292753768177, 0.0, 0.0... JVASP-75427 1103 {'nelect': 66, 'phi': 5.454720049299169, 'scf_... {'lattice_mat': [[6.8978373665407995, 0.003250... JVASP-76195 1104 {'nelect': 72, 'phi': 6.019888082326117, 'scf_... {'lattice_mat': [[5.549373162856539, -3.478901... JVASP-68932

[1105 rows x 3 columns]

Multibeam backscatter imagery extracted from gridded bathymetry of the shelf and slope environments of Jarvis Atoll, Pacific Island Areas, Central Pacific. These data provide coverage between 10 and 5000 meters. The backscatter dataset includes data collected using Simrad EM300 and Reson 8101 multibeam sonars. The sonars frequencies are 30 kHz and 240 kHz respectively and the backscatter data from each sonar are processed and gridded separately. These metadata are for the 5 m grid cell size Simrad em300 multibeam backscatter data only.

Gridded (5 m cell size) bathymetry of the shelf and slope environments of Jarvis Island, Pacific Remote Island Areas, Central Pacific. Almost complete bottom coverage was achieved in depths between 3 and 3600 meters (5 m grid includes data to 300 m). The bathymetry dataset includes Simrad EM300, EM3002D, and Reson 8101ER multibeam data collected March 20-24, 2006.

Site characterization parameters (canopy density, litter components, soil characterization: color, moisture, components) for selected sites within the Superior National Forest, MN during 1988-89

Multibeam backscatter imagery extracted from gridded bathymetry of the shelf and slope environments of Jarvis Atoll, Pacific Island Areas, Central Pacific. These data provide coverage between 10 and 5000 meters. The backscatter dataset includes data collected using Simrad EM300 and Reson 8101 multibeam sonars. The sonars frequencies are 30 kHz and 240 kHz respectively and the backscatter data from each sonar are processed and gridded separately. These metadata are for the 5 m grid cell size Simrad em300 multibeam backscatter data only.