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BisQue for 3D Materials Science in the Cloud: Microstructure–Property Linkages

  • Marat I. LatypovEmail author
  • Amil Khan
  • Christian A. Lang
  • Kris Kvilekval
  • Andrew T. Polonsky
  • McLean P. Echlin
  • Irene J. Beyerlein
  • B. S. Manjunath
  • Tresa M. Pollock
Technical Article

Abstract

Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing–structure–property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., Bioinformatics 26(4):554, 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing–structure–property relationships, can be implemented into the system. Specifically, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally efficient data-driven model into the BisQue platform. The new module is made available online (web address: https://bisque.ece.ucsb.edu/module_service/Composite_Strength/) and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.

Keywords

Materials cyberinfrastructure Cloud-based computing Reduced order models Homogenization 

Notes

Acknowledgements

Authors gratefully acknowledge the following NSF grants that supported this research: ABI No. 1356750, EAGER No. 1650972, and SI2-SSI No. 1664172. TMP also acknowledges the support of a Vannevar Bush Fellowship N00014-18-1-3031 and IJB acknowledges the support by the U.S. Dept. of Energy, Office of Basic Energy Sciences Program DE-SC0018901.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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© The Minerals, Metals & Materials Society 2019

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Marat I. Latypov
    • 1
    • 2
    Email author
  • Amil Khan
    • 3
  • Christian A. Lang
    • 3
  • Kris Kvilekval
    • 3
  • Andrew T. Polonsky
    • 1
  • McLean P. Echlin
    • 1
  • Irene J. Beyerlein
    • 1
    • 2
  • B. S. Manjunath
    • 3
  • Tresa M. Pollock
    • 1
  1. 1.Materials DepartmentUniversity of California Santa BarbaraSanta BarbaraUSA
  2. 2.Mechanical Engineering DepartmentUniversity of California Santa BarbaraSanta BarbaraUSA
  3. 3.Electrical and Computer Engineering DepartmentUniversity of California Santa BarbaraSanta BarbaraUSA

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