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Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

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Abstract

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data-driven process-structure-property (PSP) linkages provide a systemic, modular, and hierarchical framework for community-driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open-source materials data science framework that can be used to create high-value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning, and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers.

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Acknowledgments

DBB and SRK acknowledge support from NSF-IGERT Award 1258425 and NIST 70NANB14H191.

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Authors and Affiliations

  1. School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    David B Brough & Surya R. Kalidindi

  2. Materials Science and Engineering Division, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA

    Daniel Wheeler

  3. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Surya R. Kalidindi

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  1. David B Brough
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Correspondence to Surya R. Kalidindi.

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Brough, D.B., Wheeler, D. & Kalidindi, S.R. Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials. Integr Mater Manuf Innov 6, 36–53 (2017). https://doi.org/10.1007/s40192-017-0089-0

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