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nanoHUB.org: A Gateway to Undergraduate Simulation-Based Research in Materials Science and Related Fields

Published online by Cambridge University Press:  10 February 2015

Tanya A. Faltens ,
Peter Bermel ,
Amanda Buckles ,
K. Anna Douglas ,
Alejandro Strachan ,
Lynn K. Zentner  and
Gerhard Klimeck
Tanya A. Faltens
Affiliation:
Network for Computational Nanotechnology, Purdue University, West Lafayette, IN 47907, U.S.A
Peter Bermel
Affiliation:
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, U.S.A
Amanda Buckles
Affiliation:
Network for Computational Nanotechnology, Purdue University, West Lafayette, IN 47907, U.S.A
K. Anna Douglas
Affiliation:
School of Engineering Education, Purdue University, West Lafayette, IN 47907, U.S.A
Alejandro Strachan
Affiliation:
School of Materials Engineering, Purdue University, West Lafayette, IN 47907, U.S.A
Lynn K. Zentner
Affiliation:
Network for Computational Nanotechnology, Purdue University, West Lafayette, IN 47907, U.S.A
Gerhard Klimeck
Affiliation:
Network for Computational Nanotechnology, Purdue University, West Lafayette, IN 47907, U.S.A School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, U.S.A
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Abstract

Our future engineers and scientists will likely be required to use advanced simulations to solve many of tomorrow's challenges in nanotechnology. To prepare students to meet this need, the Network for Computational Nanotechnology (NCN) provides simulation-focused research experiences for undergraduates at an early point in their educational path, to increase the likelihood that they will ultimately complete a doctoral program. The NCN summer research program currently serves over 20 undergraduate students per year who are recruited nationwide, and selected by NCN and the faculty for aptitude in their chosen field within STEM, as well as complementary skills such as coding and written communication. Under the guidance of graduate student and faculty mentors, undergraduates modify or build nanoHUB simulation tools for exploring interdisciplinary problems in materials science and engineering, and related fields. While the summer projects exist within an overarching research context, the specific tasks that NCN undergraduate students engage in range from modifying existing tools to building new tools for nanoHUB and using them to conduct original research. Simulation tool development takes place within nanoHUB, using nanoHUB’s workspace, computational clusters, and additional training and educational resources. One objective of the program is for the students to publish their simulation tools on nanoHUB. These tools can be accessed and executed freely from around the world using a standard web-browser, and students can remain engaged with their work beyond the summer and into their careers. In this work, we will describe the NCN model for undergraduate summer research. We believe that our model is one that can be adopted by other universities, and will discuss the potential for others to engage undergraduate students in simulation-based research using free nanoHUB resources.

Keywords

educationsimulationSustainability
Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1762: Symposium AAA – Undergraduate Research in Materials Science—Impacts and Benefits , 2015 , mrsf14-1762-aaa06-05
Copyright
Copyright © Materials Research Society 2015 

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References

REFERENCES

Jain, Anubhav, et al. . “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation.” APL Materials 1.1 (2013): 011002.CrossRefGoogle Scholar
Roco, Mihail C., Mirkin, Chad A., and Hersam, Mark C.. “Nanotechnology research directions for societal needs in 2020.” J Nanoparticle Res 13.3 (2011): 897–919.CrossRefGoogle Scholar
Patton, Stacey, “Influx of Foreign Students Drives Modest Increase in Graduate-School Enrollments,” Chronicle of Higher Education (September 12, 2013). http://chronicle.com/article/Graduate-School-Enrollments/141577/ Google Scholar
Klimeck, Gerhard, McLennan, Michael, Brophy, Sean P., Adams, George B. III, and Lundstrom, Mark S.. “nanoHUB.org: Advancing education and research in nanotechnology.” Computing in Science & Engineering 10, no. 5 (2008): 17–23.CrossRefGoogle Scholar
Kellogg Foundation, W.K., “Logic Model Development Guide” (2004) https://www.wkkf.org/resource-directory/resource/2006/02/wk-kellogg-foundation-logic-model-development-guide Google Scholar
The 2002 User-Friendly Handbook for Project Evaluation, Directorate for Education and Human Resources, Division of Research and Learning in Formal and Informal Settings, National Science Foundation. http://www.nsf.gov/pubs/2002/nsf02057/start.htm Google Scholar
CISE REU Toolkit, UNC Charlotte. http://reu.uncc.edu/cise-reu-toolkit Google Scholar
nanoHUB.org Usage Overview (2014). https://nanohub.org/usage Google Scholar
McLennan, Michael, and Kennell, Rick. “HUBzero: a platform for dissemination and collaboration in computational science and engineering.” Computing in Science & Engineering 12.2 (2010): 48–53.CrossRefGoogle Scholar
Rappture Bootcamp Course (2014). https://nanohub.org/courses/tools Google Scholar
Rappture Bootcamp Lecture videos (2012). https://nanohub.org/resources/14671 Google Scholar
Madhavan, Krishna, Zentner, Michael, and Klimeck, Gerhard. “Learning and research in the cloud.” Nature Nanotechnology 8.11 (2013): 786–789.CrossRefGoogle Scholar
nanoHUB.org Network for Computational Nanotechnology Summer Undergraduate Research Fellowship. https://nanohub.org/groups/ncnsurf/ Google Scholar
Strachan, A., “Bayesian Simulation Tool,” (2014). https://nanohub.org/tools/bayes Google Scholar
Kang, J., Wang, X., Liu, C., and Bermel, P., “S4: Stanford Stratified Structure Solver,” (2013). https://nanohub.org/tools/s4sim/ Google Scholar
Liu, V. and Fan, S., “S4 : A free electromagnetic solver for layered periodic structures,” Comput. Phys. Commun. 183, 2233–2244 (2012).CrossRefGoogle Scholar
Khan, M. Ryyan, Wang, Xufeng, Bermel, Peter, and Alam, Muhammad A., “Enhanced light trapping in solar cells with a meta-mirror following Generalized Snell's law,” Opt. Express 22, A973–A985 (2014).CrossRefGoogle ScholarPubMed
Yablonovitch, E., Miller, O. D., and Kurtz, S. R., “A Great Solar Cell also Needs to be a Great LED: External Fluorescence Leads to New Efficiency Record,” Nobel Symposium 153: Nanoscale Energy Converters, vol. 1519, pp. 9–11 (2013).Google Scholar
Chaffee, Dalton, Wang, Xufeng, and Bermel, Peter. “Simulating Nanoscale Optics in Photovoltaics with the S-Matrix Method.” (2014).Google Scholar
Harder, Nils-P., and Green, Martin A.. “Thermophotonics.” Semiconductor Science and Technology 18.5 (2003): S270.CrossRefGoogle Scholar
Mathur, Anubha, Sakr, Enas Said, and Bermel, Peter. “Modeling Thermophotovoltaic Rare Earth Based Selective Emitters.” (2014).Google Scholar
Lent, R. W., Brown, S. D. and Hackett, G., J. Vocational Behavior 45, 79 (1994).CrossRefGoogle Scholar
Bandura, A., Self-efficacy: The exercise of control. New York: Freeman (1997).Google Scholar
nanoHUB.org Simulations and Computational Science Group (2014). https://nanohub.org/groups/simulations Google Scholar