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| Open Quantum Materials Database | |
|---|---|
| Name | Open Quantum Materials Database |
| Type | Database |
| Founded | 2013 |
| Owner | Open Quantum Materials Database Consortium |
Open Quantum Materials Database The Open Quantum Materials Database is an online repository of computed materials properties that supports discovery in condensed matter physics, materials science, and quantum engineering. It provides high-throughput density functional theory results for inorganic compounds and alloys, enabling research across institutions such as Massachusetts Institute of Technology, Harvard University, Lawrence Berkeley National Laboratory, Stanford University, and Argonne National Laboratory. The database interfaces with computational frameworks used at Oak Ridge National Laboratory, Sandia National Laboratories, California Institute of Technology, Princeton University, and Columbia University.
The database aggregates electronic structure data, formation energies, band structures, density of states, and derived quantities used by researchers at National Institute of Standards and Technology, European Organization for Nuclear Research, Max Planck Society, University of Cambridge, and Imperial College London. It interoperates with repositories and infrastructures such as Materials Project, NOMAD Repository, AFLOW Consortium, ICSD, and Citrine Informatics. The platform is integrated into workflows associated with Quantum ESPRESSO, VASP, WIEN2k, ABINIT, and SIESTA for high-throughput screening.
Initial development drew on collaborations among scientists affiliated with University of California, Berkeley, University of Illinois Urbana-Champaign, Yale University, University of Michigan, and Duke University. Funding and programmatic support involved agencies such as the U.S. Department of Energy, National Science Foundation, European Commission, Japan Science and Technology Agency, and Science and Technology Facilities Council. Early releases were coordinated alongside community efforts including the Materials Genome Initiative and partnerships with projects at Lawrence Livermore National Laboratory, Brookhaven National Laboratory, Helmholtz Association, and Riken. Key contributors and leadership included researchers connected to NREL, MIT Lincoln Laboratory, Flatiron Institute, Wellcome Trust, and Wellcome Sanger Institute.
Coverage spans ionic crystals, intermetallics, oxides, nitrides, chalcogenides, and two-dimensional materials studied by teams at Rice University, University of California, Santa Barbara, University of Texas at Austin, Cornell University, and University of Wisconsin–Madison. Entries include computed properties relevant to device research in organizations such as Intel Corporation, IBM, Samsung, TSMC, and Micron Technology. Metadata schemas reference identifiers and ontologies used by Digital Object Identifier System, ORCID, CrossRef, Europeana, and DataCite to support provenance tracking. Coverage also extends to thermodynamic phase data utilized by groups at Purdue University, Ohio State University, University of Pennsylvania, and Johns Hopkins University.
Computational protocols rely on plane-wave pseudopotential methods and projector augmented-wave frameworks developed in projects like VASP, Quantum ESPRESSO, and GPAW. Exchange-correlation choices reference approximations from Perdew–Burke–Ernzerhof, Heyd–Scuseria–Ernzerhof, and techniques linked to GW approximation, Dynamical Mean-Field Theory, and Bethe–Salpeter Equation studies conducted at ETH Zurich, École Polytechnique Fédérale de Lausanne, University of Oxford, and University of Tokyo. Workflow automation leverages tools inspired by FireWorks, Atomate, ASE, AiiDA, and integrations with high-performance computing centers such as NERSC, XSEDE, PRACE, and HPC Wales.
Users access data via web interfaces, RESTful APIs, and libraries used in software developed at Google, Microsoft Research, Facebook AI Research, Amazon Web Services, and IBM Research. Client libraries and visualization modules are compatible with environments popular at Janelia Research Campus, CERN OpenLab, Broad Institute, Sanger Institute, and European Space Agency. Authentication and identity integration utilize standards adopted by ORCID, InCommon, Shibboleth, and OpenID Foundation to support collaboration across University of California system, State University of New York, City University of New York, University of Toronto, and McGill University.
The resource has been cited in studies on photovoltaics by teams at National Renewable Energy Laboratory, University of Oxford, and Imperial College London; in catalysis research at Argonne National Laboratory, Paul Scherrer Institute, and École Normale Supérieure; and in quantum materials investigations at Los Alamos National Laboratory, Trinity College Dublin, and University of Amsterdam. Industrial adoption is evident at Tesla, Inc., BASF, Siemens, Schlumberger, and Honeywell, where computed screening accelerates materials selection. The database supports education and training in curricula at Massachusetts Institute of Technology, Stanford University, California Institute of Technology, University of Cambridge, and Imperial College London.
Governance frameworks involve advisory boards and contributors from institutions such as National Academies of Sciences, Engineering, and Medicine, Royal Society, American Physical Society, Materials Research Society, and IEEE. Licensing policies reference open-data best practices promoted by Creative Commons, Open Data Institute, Open Knowledge Foundation, SPARC, and GNU Project. Community engagement includes workshops and tutorials hosted with partners like European Materials Modelling Council, Argonne Leadership Computing Facility, Riken Center for Computational Science, UK Research and Innovation, and Australian Research Council to foster reproducibility and FAIR data principles championed by GO FAIR and Research Data Alliance.
Category:Materials databases