JARVIS: Novel NIST databases to aid Material Discovery

JARVIS (Joint Automated Repository for Various Integrated Simulations) is a unique integrated framework to accelerate material design using classical force-fields (FF), density functional theory (DFT) and machine learning (ML). The JARVIS-DFT hosts data for more than 30000 materials. We discovered more than 1500 2D materials using lattice parameter criteria and exfoliation energy calculations. We charted improved lattice parameters, formation energies and elastic tensors using van der Waals functional for more than 12000 materials and established relation between exfoliation energies and elastic constants. To alleviate bandgap underestimation in conventional DFT and improve frequency dependent dielectric function predictions, we evaluated meta-GGA based approaches for more than 10000 materials. We use spectroscopic limited maximum efficiency approach to identify potential photovoltaic materials. Using spin-orbit spillage criteria, we discovered more than 1500 potential topological materials including topological insulators, Weyl and Dirac semimetals, and topological crystalline insulators. The database is publicly available at https://jarvis.nist.gov/.