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

The JARVIS-DFT database is a collection of density functional theory (DFT) data for various materials.

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.

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, and removing redundant datapoints after calculations were performed. Band gap energies were calculated using two different DFT functionals: rPW86 and HSE06; atomization energy was calculated using rPW86. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

The JARVIS_QMOF dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Quantum Metal-Organic Frameworks (QMOF) dataset, comprising quantum-chemical properties for >14,000 experimentally synthesized MOFs. QMOF contains "DFT-ready" data: filtered to remove omitted, overlapping, unbonded or deleted atoms, along with other kinds of problematic structures commented on in the literature. Data were generated via high-throughput DFT workflow, at the PBE-D3(BJ) level of theory using VASP software. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

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-MEGNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configurations with 3D materials properties from the 2018 version of Materials Project, as used in the training of the MEGNet ML model. JARVIS is a set of tools and datasets built to meet current materials design challenges.

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 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.

The JARVIS-ML database contains machine learning models for predicting material properties.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Coral. The data include parameters of corals and sclerosponges with a geographic location of Central Pacific Ocean. The time period coverage is from 2158 to -68 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The JARVIS-2DMatPedia dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configurations with 2D materials from the 2DMatPedia database, generated through two methods: a top-down exfoliation approach, using structures of bulk materials from the Materials Project database; and a bottom-up approach, replacing each element in a 2D material with another from the same group (according to column number). JARVIS is a set of tools and datasets built to meet current materials design challenges.

The JARVIS_OMDB dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Organic Materials Database (OMDB): a dataset of 12,500 crystal materials for the purpose of training models for the prediction of properties for complex and lattice-periodic organic crystals with large numbers of atoms per unit cell. Dataset covers 69 space groups, 65 elements; averages 82 atoms per unit cell. 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 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 dataset contains the predicted prices of the asset Jarvis over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Bocconi University MSc. Data Science & Business Analytics 20600 Deep Learning for Computer Vision Team Jarvis

Object Detection Project

Test Data

This repository contains the test data used for the evaluation of the algorithms trained as part of the project. The data has been reannotated & resized to 640x640, but otherwise has not been touched. Especially, no augmentations or upsampling were done on this set. Instead, immediately after resizing and re-annotation, the train, validation & test set were split. Upsampling & augmentations were only done on the training set. Lastly, to further avoid leakage, duplicates were removed.

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.

Data has been processed by NODC to the NODC standard Bathythermograph (XBT) (C116) format. The C116/C118 format contains temperature-depth profile data obtained using expendable bathythermograph (XBT) instruments. Cruise information, position, date and time were reported for each observation. The data record was comprised of pairs of temperature-depth values. Unlike the MBT Data File, in which temperature values were recorded at uniform 5 m intervals, the XBT data files contained temperature values at non-uniform depths. These depths were recorded at the minimum number of points ("inflection points") required to accurately define the temperature curve. Standard XBTs can obtain profiles to depths of either 450 or 760 m. With special instruments, measurements can be obtained to 1830 m. Prior to July 1994, XBT data were routinely processed to one of these standard types. XBT data are now processed and loaded directly in to the NODC Ocean Profile Data Base (OPDB). Historic data from these two data types were loaded into the OPDB.

The JARVIS_SNUMAT dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains band gap data for >10,000 materials, computed using a hybrid functional and considering the stable magnetic ordering. Structure relaxation and band edges are obtained using the PBE XC functional; band gap energy is subsequently obtained using the HSE06 hybrid functional. Optical and fundamental band gap energies are included. Some gap energies are recalculated by including spin-orbit coupling. These are noted in the band gap metadata as "SOC=true". JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

Timeseries data from 'Jarvis Creek (Imiq: 10301)' (imiq_10301_ajar) _NCProperties=version=2,netcdf=4.7.4,hdf5=1.10.6 cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=,,,,satellite@gina.alaska.edu,feedback@axiomdatascience.com contributor_name=U.S. Department of Defense (DOD),US Fish and Wildlife Service (US FWS),Arctic Landscape Conservation Cooperative (Arctic LCC, defunded 2019),North Slope Science Initiative (NSSI),Geographic Information Network of Alaska (GINA),Axiom Data Science contributor_role=contributor,sponsor,sponsor,sponsor,contributor,processor contributor_role_vocabulary=NERC contributor_url=https://www.defense.gov/,https://www.fws.gov/,https://lccnetwork.org/lcc/arctic,https://northslopescience.org/,https://gina.alaska.edu/,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3 defaultDataQuery=wind_speed_qc_agg,relative_humidity_qc_agg,air_temperature,lwe_thickness_of_precipitation_amount_cm_time_sum_over_p1d_qc_agg,z,lwe_thickness_of_precipitation_amount_cm_time_sum_over_p1d,wind_speed,time,relative_humidity,wind_from_direction,air_temperature_qc_agg,wind_from_direction_qc_agg&time>=max(time)-3days Easternmost_Easting=-145.618889 featureType=TimeSeries geospatial_lat_max=63.948611 geospatial_lat_min=63.948611 geospatial_lat_units=degrees_north geospatial_lon_max=-145.618889 geospatial_lon_min=-145.618889 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from Imiq - Hydroclimate Database and Data Portal at id=112461 infoUrl=https://sensors.ioos.us/#metadata/112461/station institution=Remote Automatic Weather Stations (RAWS) naming_authority=com.axiomdatascience Northernmost_Northing=63.948611 platform=fixed platform_name=Jarvis Creek (Imiq: 10301) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=http://www.raws.dri.edu/,, sourceUrl=http://www.raws.dri.edu/ Southernmost_Northing=63.948611 standard_name_vocabulary=CF Standard Name Table v72 station_id=112461 time_coverage_end=2013-02-14T00:00:00Z time_coverage_start=2005-12-31T00:00:00Z Westernmost_Easting=-145.618889