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.
About this Dataset
Title | JARVIS: Joint Automated Repository for Various Integrated Simulations |
---|---|
Description | 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. |
Modified | 2017-10-18 |
Publisher Name | National Institute of Standards and Technology |
Contact | mailto:[email protected] |
Keywords | Density functional theory , classical interatomic potential , force-field , python , JARVIS , MGI , MDCS , RESTAPI , automation |
{ "identifier": "5BD81D0B67AA9AFAE0531A57068100201871", "accessLevel": "public", "references": [ "https:\/\/www.nature.com\/articles\/sdata2016125", "https:\/\/www.nature.com\/articles\/s41598-017-05402-0" ], "contactPoint": { "hasEmail": "mailto:[email protected]", "fn": "Kamal Choudhary" }, "programCode": [ "006:045" ], "@type": "dcat:Dataset", "landingPage": "https:\/\/www.ctcms.nist.gov\/~knc6\/JARVIS.html", "description": "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.\n\nThe 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.\n\nThe 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.\n\nThe 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.", "language": [ "en" ], "title": "JARVIS: Joint Automated Repository for Various Integrated Simulations", "distribution": [ { "accessURL": "https:\/\/www.ctcms.nist.gov\/~knc6\/JVASP.html", "format": "text\/html", "title": "JARVIS for DFT" }, { "accessURL": "https:\/\/www.ctcms.nist.gov\/~knc6\/periodic.html", "format": "text\/html", "title": "JARVIS for Force-fields" }, { "accessURL": "https:\/\/doi.org\/10.18434\/M3HQ1W" } ], "license": "https:\/\/www.nist.gov\/open\/license", "bureauCode": [ "006:55" ], "modified": "2017-10-18", "publisher": { "@type": "org:Organization", "name": "National Institute of Standards and Technology" }, "accrualPeriodicity": "irregular", "theme": [ "Physics: Condensed matter", "Materials : Modeling and computational material science", "Electronics: Thin-film electronics", "Electronics: Optoelectronics", "Chemistry: Molecular characterization", "Chemistry: Theoretical chemistry and modeling", "Chemistry: Chemical thermodynamics and chemical properties", "Electronics: Semiconductors", "Materials : Materials characterization", "Physics: Atomic, molecular, and quantum", "Physics: Optical physics" ], "keyword": [ "Density functional theory", "classical interatomic potential", "force-field", "python", "JARVIS", "MGI", "MDCS", "RESTAPI", "automation" ] }