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Provenance, workflows, and crystallographic tools in materials science: AiiDA, spglib, and seekpath

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Abstract

The near-exponential expansion in computing resources over the last few decades has enabled a rapid increase in the capabilities of computational science, including applications to materials research. In order to harness the available resources and accelerate the field of materials design, it is critically important to develop robust and reusable automation software for preparing and performing multistep computational workflows, starting with crystal structures and ending with material properties. In the domain of first-principles calculations of crystalline materials, we highlight emerging tools for automated symmetry analysis of the atomic and electronic structure. With automation capabilities in hand, the ever-increasing amount of data also becomes a serious bottleneck in terms of organization, analysis, and reproducibility. We describe some of the progress and strategic challenges in the development of a general infrastructure for coupling computational automation with data management, emphasizing data reproducibility and provenance capture.

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Acknowledgments

The authors would like to acknowledge the team of AiiDA developers,35 as well as the contributors to AiiDA, spglib, and seekpath. G.P. acknowledges support from the MARVEL NCCR of the Swiss National Science Foundation and from the EU MaX Centre of Excellence (Grant 676598); A.T. from MEXT Japan through ESISM (Elements Strategy Initiative for Structural Materials) of Kyoto University; B.K. from Robert Bosch LLC.

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Authors and Affiliations

  1. École Polytechnique Fédérale de Lausanne, Switzerland

    Giovanni Pizzi

  2. Department of Materials Science and Engineering, Kyoto University, Japan

    Atsushi Togo

  3. Harvard University, USA

    Boris Kozinsky

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  1. Giovanni Pizzi
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  2. Atsushi Togo
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Correspondence to Giovanni Pizzi.

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Pizzi, G., Togo, A. & Kozinsky, B. Provenance, workflows, and crystallographic tools in materials science: AiiDA, spglib, and seekpath. MRS Bulletin 43, 696–702 (2018). https://doi.org/10.1557/mrs.2018.203

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