Computational Materials Science and Engineering Education: An Updated Survey of Trends and Needs

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Abstract

We present a summary of the state of computational materials science and engineering (CMSE) education based on a survey of materials science department chairs, faculty with computational interests, and employers of materials scientists and engineers. This survey is an update of one previously conducted in Thornton et al. (JOM 61:12–17, 2009). Three questionnaires were distributed among department chairs, faculty, and employers. The surveys asked to rate the importance of incorporating CMSE into the undergraduate curriculum, how it should be incorporated, the current offerings in CMSE, how those offerings have recently changed, what software tools are taught/used, and what opportunities exist in CMSE education, along with freeform questions regarding experience in teaching CMSE and impact of CMSE education. The survey results revealed an increased availability of CMSE courses in most of the materials departments surveyed, and strong support for including CMSE into the core curriculum. They also indicated that there is no clear preference as to whether such incorporation into the core curriculum should be through standalone courses or through modules in existing courses. The resources used in these courses, including online tools and software, are also summarized. The responses from the computational faculty point to a continued need for modules, including software tools and educational materials, that can be readily implemented by materials faculty regardless of their area of expertise.

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References

  1. 1.

    J.M. Wing, Philos. Trans. A. Math. Phys. Eng. Sci. 366, 3717–3725 (2008).

    Article  Google Scholar 

  2. 2.

    G. Ceder and K. Persson, Sci. Am. 19, 34–40 (2013).

    Google Scholar 

  3. 3.

    https://www.mgi.gov/. Accessed 4 June 2018

  4. 4.

    K. Thornton, S. Nola, R. Edwin Garcia, M. Asta, and G.B. Olson, JOM 61, 12–17 (2009).

    Article  Google Scholar 

  5. 5.

    A.J. Magana, M.L. Falk, and M.J. Reese, ACM Trans. Comput. Educ. 13, 1–22 (2013).

    Article  Google Scholar 

  6. 6.

    C. Vieira, A. Magana, A. Roy, M. Falk, and M. Reese, ASEE Ann. Conf. Expos. Proc. 7, 26.744.1–26.744.16 (2015).

    Google Scholar 

  7. 7.

    A.J. Magana, M.L. Falk, C. Vieira, and M.J. Reese, J. Reese Comput. Human Behav. 61, 427–442 (2016).

    Article  Google Scholar 

  8. 8.

    A.J. Magana, et al., Comput. Appl. Eng. Educ. 25, 352–375 (2017).

    Article  Google Scholar 

  9. 9.

    R. Mansbach, et al., J. Mater. Educ. 38, 161–174 (2016).

    Google Scholar 

  10. 10.

    http://www.mse.engin.umich.edu/updates/newsletters/2017-mse-newsletter. Accessed 4 June 2018

  11. 11.

    https://nanohub.org/. Accessed 4 June 2018

  12. 12.

    https://icmed.engin.umich.edu/. Accessed 4 June 2018

  13. 13.

    https://cms3.tamu.edu/. Accessed 4 June 2018

Download references

Acknowledgements

This work was supported by the National Science Foundation under Grant No. 1410461.

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Correspondence to Katsuyo Thornton.

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Enrique, R.A., Asta, M. & Thornton, K. Computational Materials Science and Engineering Education: An Updated Survey of Trends and Needs. JOM 70, 1644–1651 (2018). https://doi.org/10.1007/s11837-018-2989-7

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