@Comment
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This Test Driver originally published in \cite{OpenKIM-TD:457028483760:000a, OpenKIM-TD:457028483760:000b, OpenKIM-TD:457028483760:000c} is archived in \cite{OpenKIM-TD:457028483760:000, tadmor:elliott:2011, elliott:tadmor:2011}.
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@Misc{OpenKIM-TD:457028483760:000,
  author       = {I Nikiforov and Ellad B. Tadmor},
  title        = {{E}quilibrium structure and energy for a crystal structure at zero temperature and pressure v000},
  doi          = {10.25950/53ef2ea4},
  howpublished = {OpenKIM, \url{https://doi.org/10.25950/53ef2ea4}},
  keywords     = {OpenKIM, Test Driver, TD_457028483760_000},
  publisher    = {OpenKIM},
  year         = 2023,
}

@Article{tadmor:elliott:2011,
  author    = {E. B. Tadmor and R. S. Elliott and J. P. Sethna and R. E. Miller and C. A. Becker},
  title     = {The potential of atomistic simulations and the {K}nowledgebase of {I}nteratomic {M}odels},
  journal   = {{JOM}},
  year      = {2011},
  volume    = {63},
  number    = {7},
  pages     = {17},
  doi       = {10.1007/s11837-011-0102-6},
}

@Misc{elliott:tadmor:2011,
  author       = {Ryan S. Elliott and Ellad B. Tadmor},
  title        = {{K}nowledgebase of {I}nteratomic {M}odels ({KIM}) Application Programming Interface ({API})},
  howpublished = {\url{https://openkim.org/kim-api}},
  publisher    = {OpenKIM},
  year         = 2011,
  doi          = {10.25950/ff8f563a},
}

@Article{OpenKIM-TD:457028483760:000a,
  abstract = {Empirical databases of crystal structures and thermodynamic properties are fundamental tools for materials research. Recent rapid proliferation of computational data on materials properties presents the possibility to complement and extend the databases where the experimental data is lacking or difficult to obtain. Enhanced repositories that integrate both computational and empirical approaches open novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds, metastable structures and correlations between various characteristics. The practical realization of these opportunities depends on a systematic compilation and classification of the generated data in addition to an accessible interface for the materials science community. In this paper we present an extensive repository, aflowlib.org, comprising phase-diagrams, electronic structure and magnetic properties, generated by the high-throughput framework AFLOW. This continuously updated compilation currently contains over 150,000 thermodynamic entries for alloys, covering the entire composition range of more than 650 binary systems, 13,000 electronic structure analyses of inorganic compounds, and 50,000 entries for novel potential magnetic and spintronics systems. The repository is available for the scientific community on the website of the materials research consortium, aflowlib.org.},
  author = {Curtarolo, Stefano and Setyawan, Wahyu and Wang, Shidong and Xue, Junkai and Yang, Kesong and Taylor, Richard H. and Nelson, Lance J. and Hart, Gus L.W. and Sanvito, Stefano and Buongiorno-Nardelli, Marco and Mingo, Natalio and Levy, Ohad},
  doi = {https://doi.org/10.1016/j.commatsci.2012.02.002},
  issn = {0927-0256},
  journal = {Computational Materials Science},
  keywords = {High-throughput, Combinatorial materials science, Ab initio, AFLOW, Materials genome initiative},
  pages = {227-235},
  title = {AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations},
  url = {https://www.sciencedirect.com/science/article/pii/S0927025612000687},
  volume = {58},
  year = {2012},
}

@Article{OpenKIM-TD:457028483760:000b,
  abstract = {To enable materials databases supporting computational and experimental research, it is critical to develop platforms that both facilitate access to the data and provide the tools used to generate/analyze it — all while considering the diversity of users’ experience levels and usage needs. The recently formulated FAIR principles (Findable, Accessible, Interoperable, and Reusable) establish a common framework to aid these efforts. This article describes aflow.org, a web ecosystem developed to provide FAIR-compliant access to the AFLOW databases. Graphical and programmatic retrieval methods are offered, ensuring accessibility for all experience levels and data needs. aflow.org goes beyond data-access by providing applications to important features of the AFLOW software [1], assisting users in their own calculations without the need to install the entire high-throughput framework. Outreach commitments to provide AFLOW tutorials and materials science education to a global and diverse audiences will also be presented.},
  author = {Esters, Marco and Oses, Corey and Divilov, Simon and Eckert, Hagen and Friedrich, Rico and Hicks, David and Mehl, Michael J. and Rose, Frisco and Smolyanyuk, Andriy and Calzolari, Arrigo and Campilongo, Xiomara and Toher, Cormac and Curtarolo, Stefano},
  doi = {https://doi.org/10.1016/j.commatsci.2022.111808},
  issn = {0927-0256},
  journal = {Computational Materials Science},
  keywords = {Autonomous materials science, Materials genome initiative, aflow, Computational ecosystems, Online tools, Database, Ab initio},
  pages = {111808},
  title = {aflow.org: A web ecosystem of databases, software and tools},
  url = {https://www.sciencedirect.com/science/article/pii/S0927025622005195},
  volume = {216},
  year = {2023},
}

@Article{OpenKIM-TD:457028483760:000c,
  abstract = {The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets — facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems.},
  author = {Oses, Corey and Esters, Marco and Hicks, David and Divilov, Simon and Eckert, Hagen and Friedrich, Rico and Mehl, Michael J. and Smolyanyuk, Andriy and Campilongo, Xiomara and {van de Walle}, Axel and Schroers, Jan and Kusne, A. Gilad and Takeuchi, Ichiro and Zurek, Eva and Nardelli, Marco Buongiorno and Fornari, Marco and Lederer, Yoav and Levy, Ohad and Toher, Cormac and Curtarolo, Stefano},
  doi = {https://doi.org/10.1016/j.commatsci.2022.111889},
  issn = {0927-0256},
  journal = {Computational Materials Science},
  keywords = {AFLOW, Autonomous computation, Machine learning, Workflows},
  pages = {111889},
  title = {aflow++: A C++ framework for autonomous materials design},
  url = {https://www.sciencedirect.com/science/article/pii/S0927025622006000},
  volume = {217},
  year = {2023},
}