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Summary
JARVIS-DFT (Website: https://www.ctcms.nist.gov/~knc6/JVASP.html ) is a materials property repository focused on density functional theory (DFT) predictions of material properties, especially for crystalline materials. JARVIS-DFT is a part of the NIST-JARVIS project (https://jarvis.nist.gov/).
Description
JARVIS-DFT hosts materials property data for ~40000 bulk and ~1000 low-dimensional crystalline materials and the database is continuously expanding. Some of the properties in the database are: formation energies, bandgaps, elastic, piezoelectric, dielectric constants, and magnetic moments, exfoliation energies for van der Waals bonded materials, 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. Additional resources are available at:
- Computational tools: https://github.com/usnistgov/jarvis
- Metadata: https://figshare.com/authors/Kamal_Choudhary/4445539
- Presentation slides: https://www.slideshare.net/KAMALCHOUDHARY4
Major Accomplishments
Recent and Upcoming Publications:
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JARVIS: An Integrated Infrastructure for Data-driven Materials Design, https://arxiv.org/abs/2007.01831
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Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods, Chem. Mater., 31, 15, 5900 (2019). https://doi.org/10.1021/acs.chemmater.9b02166
- Data-driven Discovery of 3D and 2D Thermoelectric Materials, arXiv:1906.06024
- High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses, npj Computational Materials, 6, 64 (2020). https://doi.org/10.1038/s41524-020-0337-2
- Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy, https://arxiv.org/abs/1912.09027.
- Computational Search for Magnetic and Non-magnetic 2D Topological Materials using Unified Spin-orbit Spillage Screening , npj Computational Materials, 6, 49 (2020). https://doi.org/10.1038/s41524-020-0319-4
- Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning, https://arxiv.org/abs/2004.03025.
- Density Functional Theory based Electric Field Gradient Database, https://arxiv.org/abs/2005.09255.