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Online simulation powered learning modules for materials science

Published online by Cambridge University Press:  03 July 2019

Samuel Temple Reeve ,
David M. Guzman ,
Lorena Alzate-Vargas ,
Benjamin Haley ,
Peilin Liao  and
Alejandro Strachan
Samuel Temple Reeve*
Affiliation:
Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA94550
David M. Guzman
Affiliation:
Condensed Matter Physics & Materials Science Division, Brookhaven National Laboratory, Upton
Lorena Alzate-Vargas
Affiliation:
School of Materials Engineering and Birck Nanotechnology Center, Purdue University,West Lafayette, Indiana 47906, USA
Benjamin Haley
Affiliation:
School of Materials Engineering and Birck Nanotechnology Center, Purdue University,West Lafayette, Indiana 47906, USA
Peilin Liao
Affiliation:
School of Materials Engineering and Birck Nanotechnology Center, Purdue University,West Lafayette, Indiana 47906, USA
Alejandro Strachan
Affiliation:
School of Materials Engineering and Birck Nanotechnology Center, Purdue University,West Lafayette, Indiana 47906, USA
*
*(Email: [email protected])
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Abstract

Simulation tools are playing an increasingly important role in materials science and engineering and beyond their well established importance in research and development, these tools have a significant pedagogical potential. We describe a set of online simulation tools and learning modules designed to help students explore important concepts in materials science where hands-on activities with high-fidelity simulations can provide insight not easily acquired otherwise. The online tools, which involve density functional theory and molecular dynamics simulations, have been designed with non-expert end-users in mind and only a few clicks are required to perform most simulations, yet they are powered by research-grade codes and expert users can access advanced options. All tools and modules are available for online simulation in nanoHUB.org and access is open and free of charge. Importantly, instructors and students do not need to download or install any software. The learning modules cover a range of topics from electronic structure of crystals and doping, plastic deformation in metals, and physical properties of polymers. These modules have been used in several core undergraduate courses at Purdue’s School of Materials Engineering, they are self contained, and are easy to incorporate into existing classes.

Keywords

simulationeducation
Type
Articles
Information
MRS Advances , Volume 4 , Issue 50: Broader Impact/Characterization, Processing and Theory , 2019 , pp. 2727 - 2742
Copyright
Copyright © Materials Research Society 2019 

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References

References:

Allison, J., Backman, D., and Christodoulou, L., “Integrated computational materials engineering: A new paradigm for the global materials profession,” JOM, vol. 58, no. 11, pp. 25–27, Nov. 2006.CrossRefGoogle Scholar
US National Science and Technology Council, “Materials Genome Initiative for Global Competitiveness,”US National Science and Technology Council, 2011.Google Scholar
Thornton, K., Nola, S., Edwin Garcia, R., Asta, M., and Olson, G. B., “Computational materials science and engineering education: A survey of trends and needs,” JOM, vol. 61, no. 10, p. 12, Oct. 2009.CrossRefGoogle Scholar
Enrique, R. A., Asta, M., and Thornton, K., “Computational Materials Science and Engineering Education: An Updated Survey of Trends and Needs,” JOM, vol. 70, no. 9, pp. 1644–1651, Sep. 2018.CrossRefGoogle Scholar
Kononov, A., Bellon, P., Bretl, T., Ferguson, A. L., Herman, G. L., Killian, K. A., & West, M., “Computational curriculum for MatSE undergraduates,” ASEE Annu. Conf. Expo. Conf. Proc., vol. 2017-June, 2017.Google Scholar
Plimpton, S., “Fast Parallel Algorithms for Short-Range Molecular Dynamics,” Journal of Computational Physics, vol. 117, no. 1, pp. 1–19, Mar. 1995.CrossRefGoogle Scholar
Giannozzi, P., Baroni, S., Bonini, N., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Chiarotti, G. L., Cococcioni, M., Dabo, I., Dal Corso, A., de Gironcoli, S., Fabris, S., Fratesi, G., Gebauer, R., Gerstmann, U., Gougoussis, C., Kokalj, A., Lazzeri, M., Martin-Samos, L., Marzari, N., Mauri, F., Mazzarello, R., Paolini, S., Pasquarello, A., Paulatto, L., Sbraccia, C., Scandolo, S., Sclauzero, G., Seitsonen, A. P., Smogunov, A., Umari, P., and Wentzcovitch, R. M., “QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials,” J. Phys.: Condens. Matter, vol. 21, no. 39, p. 395502, Sep. 2009.Google Scholar
Giannozzi, P., Andreussi, O., Brumme, T., Bunau, O., Buongiorno Nardelli, M., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Cococcioni, M., Colonna, N., Carnimeo, I., Dal Corso, A., de Gironcoli, S., Delugas, P., DiStasio, R. A. Jr, Ferretti, A., Floris, A., Fratesi, G., Fugallo, G., Gebauer, R., Gerstmann, U., Giustino, F., Gorni, T., Jia, J., Kawamura, M., Ko, H.-Y., Kokalj, A., Küçükbenli, E., Lazzeri, M., Marsili, M., Marzari, N., Mauri, F., Nguyen, N. L., Nguyen, H.-V., Otero-de-la-Roza, A., Paulatto, L., Poncé, S., Rocca, D., Sabatini, R., Santra, B., Schlipf, M., Seitsonen, A. P., Smogunov, A., Timrov, I., Thonhauser, T., Umari, P., Vast, N., Wu, X., and Baroni, S., “Advanced capabilities for materials modelling with Quantum ESPRESSO,” J. Phys.: Condens. Matter, vol. 29, no. 46, p. 465901, Oct. 2017.Google ScholarPubMed
Magana, A. J., Strachan, A., and Brophy, S. P., “Lectures and Simulation Laboratories to improve Learners’ Conceptual Understanding,” Adv. Eng. Educ., vol. 3, no. 3, 2013.Google Scholar
Coughlan, A., Diefes-Dux, H. A., Douglas, K. A., Faltens, T. A., and Johnson, D., “The Continuing Effort to Enhanced Learning of Mechanical Behavior of Materials via Combined Experiments and nanoHUB Simulations: Learning Modules for Sophomore MSE Students,” MRS Advances, vol. 1, no. 56, pp. 3721–3726, ed 2016.CrossRefGoogle Scholar
Strachan, A., Klimeck, G., and Lundstrom, M., “Cyber-Enabled Simulations in Nanoscale Science and Engineering,” Computing in Science & Engineering, vol. 12, no. 2, pp. 12–17, Mar. 2010.CrossRefGoogle Scholar
Javier, G., Kamran, U., Guzman, D., Strachan, A., and Liao, P., “DFT Material Properties Simulator,” 2017. Available: https://nanohub.org/resources/dftmatprop DOI: 10.21981/D30G3H12Q. [Accessed: 06-Mar-2019].CrossRefGoogle Scholar
Kohn, W. and Sham, L. J., “Self-Consistent Equations Including Exchange and Correlation Effects,” Phys. Rev., vol. 140, no. 4A, pp. A1133–A1138, Nov. 1965.CrossRefGoogle Scholar
Hohenberg, P. and Kohn, W., “Inhomogeneous Electron Gas,” Phys. Rev., vol. 136, no. 3B, pp. B864–B871, Nov. 1964.CrossRefGoogle Scholar
Troullier, N. and Martins, J. L., “Efficient pseudopotentials for plane-wave calculations,” Phys. Rev. B, vol. 43, no. 3, pp. 1993–2006, Jan. 1991.Google Scholar
Perdew, J. P. and Zunger, A., “Self-interaction correction to density-functional approximations for many-electron systems,” Phys. Rev. B, vol. 23, no. 10, pp. 5048–5079, May 1981.CrossRefGoogle Scholar
Perdew, J. P., Burke, K., and Ernzerhof, M., “Generalized Gradient Approximation Made Simple,” Phys. Rev. Lett., vol. 77, no. 18, pp. 3865–3868, Oct. 1996.CrossRefGoogle ScholarPubMed
Murnaghan, F. D., “The Compressibility of Media under Extreme Pressures,” Proc Natl Acad Sci U S A, vol. 30, no. 9, pp. 244–247, Sep. 1944.CrossRefGoogle ScholarPubMed
Conrad, K., Maassen, J., and Lundstrom, M., “LanTraP,” 2014. Available: https://nanohub.org/resources/lantrap DOI: 10.4231/D3NP1WJ64. [Accessed: 06-Mar-2019].CrossRefGoogle Scholar
Reeve, S. T., Chow, C., Sakano, M. N., Tang, S., Belessiotis, A., Wood, M., Banlusan, K., Desai, S., and Strachan, A., “Nanomaterial Mechanics Explorer,” 2018. Available: https://nanohub.org/resources/nanomatmech DOI: 10.21981/3T79-AT52. [Accessed: 06-Mar-2019].CrossRefGoogle Scholar
Alder, B. J. and Wainwright, T. E., “Phase Transition for a Hard Sphere System,” J. Chem. Phys., vol. 27, no. 5, pp. 1208–1209, Nov. 1957.CrossRefGoogle Scholar
Tadmor, E. B., Elliott, R. S., Phillpot, S. R., and Sinnott, S. B., “NSF cyberinfrastructures: A new paradigm for advancing materials simulation,” Curr Opin Solid State Mater Sci, vol. 17, no. 6, pp. 298–304, 2013.CrossRefGoogle Scholar
Hirel, P., “Atomsk: A tool for manipulating and converting atomic data files,” Comput Phys Commun, vol. 197, pp. 212–219, 2015.CrossRefGoogle Scholar
Stukowski, A., “Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool,” Modelling Simul. Mater. Sci. Eng., vol. 18, no. 1, p. 015012, 2010.CrossRefGoogle Scholar
Daw, M. S. and Baskes, M. I., “Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals,” Phys Rev B, vol. 29, no. 12, pp. 6443–6453, 1984.CrossRefGoogle Scholar
Haley, B., Wilson, N., Li, C., Arguelles, A., Jaramillo, E., and Strachan, A., “Polymer Modeler,” 2018. Available: https://nanohub.org/resources/polymod/ DOI: 10.21981/D3M03Z05V. [Accessed: 06-Mar-2019].CrossRefGoogle Scholar
Haley, B. P., Li, C., Wilson, N., Jaramillo, E., and Strachan, A., “Atomistic simulations of amorphous polymers in the cloud with PolymerModeler,” ArXiv150303894 Cond-Mat Physicsphysics, Mar. 2015.Google Scholar
Sadanobu, J. and Goddard, W. A. III, “The continuous configurational Boltzmann biased direct Monte Carlo method for free energy properties of polymer chains,” J. Chem. Phys., vol. 106, no. 16, pp. 6722–6729, Apr. 1997.CrossRefGoogle Scholar
Mayo, S. L., Olafson, B. D., and Goddard, W. A., “DREIDING: a generic force field for molecular simulations,” J Phys Chem, vol. 94, no. 26, pp. 8897–8909, Dec. 1990.CrossRefGoogle Scholar
van Duin, A. C. T., Dasgupta, S., Lorant, F., and Goddard, W. A. III, “ReaxFF: A reactive force field for hydrocarbons,” J Phys Chem A, vol. 105, no. 41, pp. 9396–9409, 2001.CrossRefGoogle Scholar