@article{c2db,
        doi = {10.1088/2053-1583/aacfc1},
        url = {https://doi.org/10.1088%2F2053-1583%2Faacfc1},
        year = 2018,
        month = {sep},
        publisher = {{IOP} Publishing},
        volume = {5},
        number = {4},
        pages = {042002},
        author = {Sten Haastrup and Mikkel Strange and Mohnish Pandey and Thorsten Deilmann and Per S Schmidt and Nicki F Hinsche and Morten N Gjerding and Daniele Torelli and Peter M Larsen and Anders C Riis-Jensen and Jakob Gath and Karsten W Jacobsen and Jens J{\o}rgen Mortensen and Thomas Olsen and Kristian S Thygesen},
        title = {The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals},
        journal = {2D Materials},
        abstract = {We introduce the Computational 2D Materials Database (C2DB), which organises a variety of structural, thermodynamic, elastic, electronic, magnetic, and optical properties of around 1500 two-dimensional materials distributed over more than 30 different crystal structures. Material properties are systematically calculated by state-of-the-art density functional theory and many-body perturbation theory ( and the Bethe–Salpeter equation for  ∼250 materials) following a semi-automated workflow for maximal consistency and transparency. The C2DB is fully open and can be browsed online (http://c2db.fysik.dtu.dk) or downloaded in its entirety. In this paper, we describe the workflow behind the database, present an overview of the properties and materials currently available, and explore trends and correlations in the data. Moreover, we identify a large number of new potentially synthesisable 2D materials with interesting properties targeting applications within spintronics, (opto-)electronics, and plasmonics. The C2DB offers a comprehensive and easily accessible overview of the rapidly expanding family of 2D materials and forms an ideal platform for computational modeling and design of new 2D materials and van der Waals heterostructures.}
}

@INPROCEEDINGS{slurm,
    author = {Morris A. Jette and Andy B. Yoo and Mark Grondona},
    title = {SLURM: Simple Linux Utility for Resource Management},
    booktitle = {In Lecture Notes in Computer Science: Proceedings of Job Scheduling Strategies for Parallel Processing ({JSSPP}) 2003},
    year = {2002},
    pages = {44--60},
    publisher = {Springer-Verlag},
    doi = {10.1007/10968987_3}
}

@article{felix,
author = "Felix Tim Bölle and Nicolai Rask Mathiesen and Alexander Juul Nielsen and Tejs Vegge and Juan Maria García Lastra and Ivano E. Castelli",
title = "{Autonomous Discovery of Materials for Intercalation Electrodes}",
year = "2019",
month = "10",
url = "https://chemrxiv.org/articles/Autonomous_Discovery_of_Materials_for_Intercalation_Electrodes/9971054",
doi = "10.26434/chemrxiv.9971054.v1"
}

@article {fireworks,
author = {Jain, Anubhav and Ong, Shyue Ping and Chen, Wei and Medasani, Bharat and Qu, Xiaohui and Kocher, Michael and Brafman, Miriam and Petretto, Guido and Rignanese, Gian-Marco and Hautier, Geoffroy and Gunter, Daniel and Persson, Kristin A.},
title = {FireWorks: a dynamic workflow system designed for high-throughput applications},
journal = {Concurrency and Computation: Practice and Experience},
volume = {27},
number = {17},
issn = {1532-0634},
url = {http://dx.doi.org/10.1002/cpe.3505},
doi = {10.1002/cpe.3505},
pages = {5037--5059},
keywords = {scientific workflows, high-throughput computing, fault-tolerant computing},
year = {2015},
note = {CPE-14-0307.R2},
}

@article{aiida,
title = "AiiDA: automated interactive infrastructure and database for computational science",
journal = "Computational Materials Science",
volume = "111",
pages = "218 - 230",
year = "2016",
issn = "0927-0256",
doi = "https://doi.org/10.1016/j.commatsci.2015.09.013",
url = "http://www.sciencedirect.com/science/article/pii/S0927025615005820",
author = "Giovanni Pizzi and Andrea Cepellotti and Riccardo Sabatini and Nicola Marzari and Boris Kozinsky",
keywords = "High-throughput, Materials database, Scientific workflow, Directed acyclic graph, Provenance, Reproducibility",
abstract = "Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA’s design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA’s design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows."
}
